from IPython.display import Image

# <img src="../images/analisis_outlier.png">
Image(filename="../images/analisis_outlier.png")

Image(filename="../images/missing.png")

import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
import plotly.express as px
import time
import gc
import psutil
from sklearn.impute import KNNImputer
import scipy.stats as ss
import warnings
from sklearn.model_selection import train_test_split
from scipy.stats import chi2_contingency

pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 5000)
seed = np.random.seed(123)

import os
print(os.getcwd())

C:\Users\ninao\Desktop\MachineLearning\MachineLearning\notebooks

import sys
sys.path.append("../src")  
import funciones_auxiliares as f_aux

df_application = pd.read_csv("../data/preprocessed_data/bank_data_preprocessed.csv")

df_application.shape

(307511, 122)

df_application.columns

Index(['COMMONAREA_MEDI', 'COMMONAREA_AVG', 'COMMONAREA_MODE',
       'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAPARTMENTS_AVG',
       'NONLIVINGAPARTMENTS_MEDI', 'FONDKAPREMONT_MODE',
       'LIVINGAPARTMENTS_MODE', 'LIVINGAPARTMENTS_AVG',
       'LIVINGAPARTMENTS_MEDI',
       ...
       'DAYS_ID_PUBLISH', 'DAYS_REGISTRATION', 'DAYS_EMPLOYED', 'DAYS_BIRTH',
       'REGION_POPULATION_RELATIVE', 'NAME_HOUSING_TYPE', 'NAME_FAMILY_STATUS',
       'NAME_EDUCATION_TYPE', 'NAME_INCOME_TYPE', 'SK_ID_CURR'],
      dtype='object', length=122)

list_var_cat, other = f_aux.dame_variables_categoricas(dataset=df_application)
df_application[list_var_cat] = df_application[list_var_cat].astype("category")
list_var_continuous = list(df_application.select_dtypes('float').columns)
df_application[list_var_continuous] = df_application[list_var_continuous].astype(float)
df_application.dtypes

COMMONAREA_MEDI                  float64
COMMONAREA_AVG                   float64
COMMONAREA_MODE                  float64
NONLIVINGAPARTMENTS_MODE         float64
NONLIVINGAPARTMENTS_AVG          float64
NONLIVINGAPARTMENTS_MEDI         float64
FONDKAPREMONT_MODE              category
LIVINGAPARTMENTS_MODE            float64
LIVINGAPARTMENTS_AVG             float64
LIVINGAPARTMENTS_MEDI            float64
FLOORSMIN_AVG                    float64
FLOORSMIN_MODE                   float64
FLOORSMIN_MEDI                   float64
YEARS_BUILD_MEDI                 float64
YEARS_BUILD_MODE                 float64
YEARS_BUILD_AVG                  float64
OWN_CAR_AGE                      float64
LANDAREA_MEDI                    float64
LANDAREA_MODE                    float64
LANDAREA_AVG                     float64
BASEMENTAREA_MEDI                float64
BASEMENTAREA_AVG                 float64
BASEMENTAREA_MODE                float64
EXT_SOURCE_1                     float64
NONLIVINGAREA_MODE               float64
NONLIVINGAREA_AVG                float64
NONLIVINGAREA_MEDI               float64
ELEVATORS_MEDI                   float64
ELEVATORS_AVG                    float64
ELEVATORS_MODE                   float64
WALLSMATERIAL_MODE              category
APARTMENTS_MEDI                  float64
APARTMENTS_AVG                   float64
APARTMENTS_MODE                  float64
ENTRANCES_MEDI                   float64
ENTRANCES_AVG                    float64
ENTRANCES_MODE                   float64
LIVINGAREA_AVG                   float64
LIVINGAREA_MODE                  float64
LIVINGAREA_MEDI                  float64
HOUSETYPE_MODE                  category
FLOORSMAX_MODE                   float64
FLOORSMAX_MEDI                   float64
FLOORSMAX_AVG                    float64
YEARS_BEGINEXPLUATATION_MODE     float64
YEARS_BEGINEXPLUATATION_MEDI     float64
YEARS_BEGINEXPLUATATION_AVG      float64
TOTALAREA_MODE                   float64
EMERGENCYSTATE_MODE             category
OCCUPATION_TYPE                 category
EXT_SOURCE_3                     float64
AMT_REQ_CREDIT_BUREAU_HOUR       float64
AMT_REQ_CREDIT_BUREAU_DAY        float64
AMT_REQ_CREDIT_BUREAU_WEEK       float64
AMT_REQ_CREDIT_BUREAU_MON        float64
AMT_REQ_CREDIT_BUREAU_QRT        float64
AMT_REQ_CREDIT_BUREAU_YEAR       float64
NAME_TYPE_SUITE                 category
OBS_30_CNT_SOCIAL_CIRCLE         float64
DEF_30_CNT_SOCIAL_CIRCLE         float64
OBS_60_CNT_SOCIAL_CIRCLE         float64
DEF_60_CNT_SOCIAL_CIRCLE         float64
EXT_SOURCE_2                     float64
AMT_GOODS_PRICE                  float64
AMT_ANNUITY                      float64
CNT_FAM_MEMBERS                  float64
DAYS_LAST_PHONE_CHANGE           float64
CNT_CHILDREN                    category
FLAG_DOCUMENT_8                 category
NAME_CONTRACT_TYPE              category
CODE_GENDER                     category
FLAG_OWN_CAR                    category
FLAG_DOCUMENT_2                 category
FLAG_DOCUMENT_3                 category
FLAG_DOCUMENT_4                 category
FLAG_DOCUMENT_5                 category
FLAG_DOCUMENT_6                 category
FLAG_DOCUMENT_7                 category
FLAG_DOCUMENT_9                 category
FLAG_DOCUMENT_21                category
FLAG_DOCUMENT_10                category
FLAG_DOCUMENT_11                category
FLAG_OWN_REALTY                 category
FLAG_DOCUMENT_13                category
FLAG_DOCUMENT_14                category
FLAG_DOCUMENT_15                category
FLAG_DOCUMENT_16                category
FLAG_DOCUMENT_17                category
FLAG_DOCUMENT_18                category
FLAG_DOCUMENT_19                category
FLAG_DOCUMENT_20                category
FLAG_DOCUMENT_12                category
AMT_CREDIT                       float64
AMT_INCOME_TOTAL                 float64
FLAG_PHONE                      category
LIVE_CITY_NOT_WORK_CITY         category
REG_CITY_NOT_WORK_CITY          category
TARGET                          category
REG_CITY_NOT_LIVE_CITY          category
LIVE_REGION_NOT_WORK_REGION     category
REG_REGION_NOT_WORK_REGION      category
REG_REGION_NOT_LIVE_REGION      category
HOUR_APPR_PROCESS_START         category
WEEKDAY_APPR_PROCESS_START      category
REGION_RATING_CLIENT_W_CITY     category
REGION_RATING_CLIENT            category
FLAG_EMAIL                      category
FLAG_CONT_MOBILE                category
ORGANIZATION_TYPE               category
FLAG_WORK_PHONE                 category
FLAG_EMP_PHONE                  category
FLAG_MOBIL                      category
DAYS_ID_PUBLISH                    int64
DAYS_REGISTRATION                float64
DAYS_EMPLOYED                      int64
DAYS_BIRTH                         int64
REGION_POPULATION_RELATIVE       float64
NAME_HOUSING_TYPE               category
NAME_FAMILY_STATUS              category
NAME_EDUCATION_TYPE             category
NAME_INCOME_TYPE                category
SK_ID_CURR                         int64
dtype: object

df_application.describe()

pd_plot_target_status = df_application['TARGET']\
        .value_counts(normalize=True)\
        .mul(100).rename('percent').reset_index()

pd_plot_target_status_conteo = df_application['TARGET'].value_counts().reset_index()
pd_plot_target_status_pc = pd.merge(pd_plot_target_status, 
                                  pd_plot_target_status_conteo, on=['index'], how='inner')

fig = px.histogram(pd_plot_target_status_pc, x="index", y=['percent'])
fig.show()

X_df_application, X_df_application_test, y_df_application, y_df_application_test = train_test_split(df_application.drop('TARGET',axis=1), 
                                                                     df_application['TARGET'], 
                                                                     stratify=df_application['TARGET'], 
                                                                     test_size=0.2)
df_application_train = pd.concat([X_df_application, y_df_application],axis=1)
df_application_test = pd.concat([X_df_application_test, y_df_application_test],axis=1)

print('== Train\n', df_application_train['TARGET'].value_counts(normalize=True))
print('== Test\n', df_application_test['TARGET'].value_counts(normalize=True))

== Train
 0    0.919271
1    0.080729
Name: TARGET, dtype: float64
== Test
 0    0.919272
1    0.080728
Name: TARGET, dtype: float64

pd_series_null_columns = df_application_train.isnull().sum().sort_values(ascending=False)
pd_series_null_rows = df_application_train.isnull().sum(axis=1).sort_values(ascending=False)
print(pd_series_null_columns.shape, pd_series_null_rows.shape)

pd_null_columnas = pd.DataFrame(pd_series_null_columns, columns=['nulos_columnas'])     
pd_null_filas = pd.DataFrame(pd_series_null_rows, columns=['nulos_filas'])  
pd_null_filas['target'] = df_application_train['TARGET'].copy()
pd_null_columnas['porcentaje_columnas'] = pd_null_columnas['nulos_columnas']/df_application_train.shape[0]
pd_null_filas['porcentaje_filas']= pd_null_filas['nulos_filas']/df_application_train.shape[1]

(122,) (246008,)

pd_null_columnas

pd_null_filas.head()

def set_plot_style():
    """Configura el estilo general de los gráficos"""
    sns.set_style("whitegrid")
    plt.rcParams['figure.dpi'] = 100
    plt.rcParams['font.size'] = 10
    plt.rcParams['axes.titlesize'] = 12
    plt.rcParams['axes.labelsize'] = 10
    plt.rcParams['xtick.labelsize'] = 9
    plt.rcParams['ytick.labelsize'] = 9

def clear_memory():
    """Limpia la memoria"""
    plt.close('all')
    gc.collect()

def optimize_figure_size(n_categories, label_length):
    """Optimiza el tamaño de la figura"""
    if n_categories > 10 or label_length > 15:
        return (15, 6)
    return (12, 5)

def plot_feature(df, col_name, isContinuous, target, max_categories=15, sample_size=50000):
    
    """
   Función para crear gráficos exploratorios personalizados con Seaborn, optimizando su diseño según las 
    características de la variable analizada (continua o categórica) y su relación con la variable objetivo.

    Parámetros:
    -----------
    - df : pandas.DataFrame
        El conjunto de datos que contiene las variables a analizar.
    - col_name : str
        Nombre de la columna a analizar dentro del DataFrame.
    - isContinuous : bool
        Indica si la columna es una variable continua (True) o categórica (False).
    - target : str
        Nombre de la variable objetivo con la que se analizará la relación de la columna.
    - max_categories : int, opcional (default=15)
        Número máximo de categorías a visualizar para variables categóricas (las categorías con menor frecuencia serán excluidas si se excede este límite).
    - sample_size : int, opcional (default=50000)
        Tamaño máximo de la muestra a utilizar para optimizar el rendimiento en conjuntos de datos grandes.      
        
    Salida:
    -------
    - fig : matplotlib.figure.Figure
        Figura generada con los gráficos. Si ocurre un error durante la generación de los gráficos, 
        se devuelve None.

    Descripción del Proceso:
    ------------------------
    1. *Reducción de datos para conjuntos grandes*: Si el número de filas del DataFrame supera 
       sample_size, se toma una muestra aleatoria para optimizar la velocidad de procesamiento.
    2. *Ajuste del tamaño de la figura*: El tamaño de los gráficos se calcula dinámicamente en función 
       del número de valores únicos en la columna analizada (col_name) y la longitud de sus etiquetas.
    3. *Gráficos para variables continuas*:
       - *Histograma*: Muestra la distribución de la variable. Incluye líneas para la media y la mediana.
       - *Boxplot*: Visualiza la distribución de la variable por categorías de la variable objetivo.
    4. *Gráficos para variables categóricas*:
       - *Conteo de categorías*: Muestra la frecuencia de las principales categorías.
       - *Proporción por target*: Visualiza la proporción de la variable objetivo en función de las categorías.
    5. *Limpieza de outliers (para continuas)*: Se excluyen valores extremos utilizando los percentiles 
       1 y 99.
    6. *Control de etiquetas y rotación*: Se optimizan las etiquetas del eje X para mejorar la legibilidad, ajustando la rotación según la longitud de las etiquetas y el número de categorías.
  Uso recomendado:
    -----------------
    Utiliza esta función para el análisis exploratorio de datos (EDA), cuando necesites entender:
    - La distribución de una variable continua o categórica.
    - La relación entre una variable independiente y la variable objetivo.
    
    Manejo de errores:
    ------------------
    Si ocurre un error durante la ejecución (por ejemplo, si una columna no existe en el DataFrame o si 
    el tipo de dato no es adecuado), la función cierra la figura creada y devuelve None.  
    """
    
    # Muestreo si el dataset es grande
    if len(df) > sample_size:
        df = df.sample(n=sample_size, random_state=42)
    
    # Calcular métricas para el tamaño
    n_unique = len(df[col_name].unique())
    max_label_len = df[col_name].astype(str).str.len().max()
    figsize = optimize_figure_size(n_unique, max_label_len)
    
    # Crear figura
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=figsize)
    
    # Paleta de colores personalizada
    palette = sns.color_palette(['#2ecc71', '#e74c3c', '#3498db'])
    
    try:
        # Calcular información de nulos
        nulls = df[col_name].isnull().sum()
        null_pct = (nulls / len(df)) * 100
        
        if isContinuous:
            # Limpiar outliers
            q1, q3 = df[col_name].quantile([0.01, 0.99])
            df_clean = df[(df[col_name] >= q1) & (df[col_name] <= q3)]
            
            # Gráfico de distribución
            sns.histplot(
                data=df_clean,
                x=col_name,
                ax=ax1,
                color=palette[0],
                kde=True,
                bins=30
            )
            
            # Añadir líneas de media y mediana
            mean_val = df_clean[col_name].mean()
            median_val = df_clean[col_name].median()
            ax1.axvline(mean_val, color=palette[1], linestyle='--', label=f'Media: {mean_val:.2f}')
            ax1.axvline(median_val, color=palette[2], linestyle='--', label=f'Mediana: {median_val:.2f}')
            ax1.legend(fontsize=8)
            
            # Boxplot
            sns.boxplot(
                x=target,
                y=col_name,
                data=df_clean,
                ax=ax2,
                palette=[palette[0], palette[1]]
            )
            
        else:
            # Limitar categorías para variables categóricas
            value_counts = df[col_name].value_counts()
            if len(value_counts) > max_categories:
                top_cats = value_counts.nlargest(max_categories).index
                df = df[df[col_name].isin(top_cats)]
                value_counts = value_counts[top_cats]
                
            # Gráfico de conteo
            sns.countplot(
                data=df,
                x=col_name,
                order=value_counts.index,
                ax=ax1,
                color=palette[0]
            )
            
            # Gráfico de proporción por target
            target_props = df.groupby(col_name)[target].value_counts(normalize=True).unstack()
            target_props.plot(
                kind='bar',
                ax=ax2,
                color=[palette[0], palette[1]],
                width=0.8
            )
            ax2.set_ylabel(f'Proporción de {target}')
        
        # Configurar títulos y etiquetas
        ax1.set_title(f'Distribución de {col_name}\nNulos: {nulls:,} ({null_pct:.1f}%)')
        ax2.set_title(f'Relación con {target}')
        
        # Ajustar etiquetas
        for ax in [ax1, ax2]:
            if not isContinuous:
                labels = ax.get_xticklabels()
                if len(labels) > 0:
                    max_len = max(len(str(l.get_text())) for l in labels)
                    rotation = 90 if max_len > 10 or len(labels) > 5 else 45
                    ax.set_xticklabels(
                        labels, 
                        rotation=rotation, 
                        ha='right' if rotation == 90 else 'right',
                        fontsize=8
                    )
            
            # Ajustar grid
            ax.grid(True, alpha=0.3)
            
            # Ajustar límites
            if not isContinuous:
                ax.set_xlabel(col_name, fontsize=9)
        
        # Ajustar espacio
        plt.tight_layout()
        return fig
        
    except Exception as e:
        plt.close(fig)
        print(f"Error en {col_name}: {str(e)}")
        return None

def plot_all_features(df, target_col='TARGET', batch_size=3, memory_threshold=85):
    """
    Función para generar gráficos exploratorios de todas las características de un conjunto de datos,
    optimizando el uso de recursos de memoria y procesando las columnas en lotes.

    Parámetros:
    -----------
    - df : pandas.DataFrame
        El conjunto de datos que contiene las características y la variable objetivo.
    - target_col : str, opcional (default='TARGET')
        Nombre de la columna objetivo con la cual se analizará la relación de las demás características.
    - batch_size : int, opcional (default=3)
        Número de columnas a procesar simultáneamente en cada lote para optimizar el rendimiento.
    - memory_threshold : int, opcional (default=85)
        Porcentaje máximo de uso de memoria permitido antes de forzar la limpieza de recursos.  
        
    Descripción del Proceso:
    ------------------------
    1. *Configuración inicial*:
       - Configura el estilo visual para los gráficos utilizando una función personalizada (set_plot_style).
       - Excluye la columna objetivo (target_col) de la lista de columnas a procesar.
       - Calcula el número total de columnas a analizar.

    2. *Procesamiento por lotes*:
       - Divide las columnas en lotes (batch_size) para evitar sobrecargar la memoria en datasets grandes.
       - Antes de procesar un lote, verifica el uso de memoria utilizando psutil.virtual_memory().
       - Si el uso de memoria excede el umbral definido (memory_threshold), fuerza la limpieza de memoria 
         invocando clear_memory().

    3. *Generación de gráficos*:
       - Para cada columna del lote:
         - Determina si es una variable continua o categórica, basándose en su tipo de datos y el número de 
           valores únicos.
         - Llama a la función plot_feature para generar los gráficos correspondientes. Si la función devuelve 
           un gráfico válido, este se muestra y se cierra para liberar recursos.
       - Limpia la memoria después de procesar cada columna para optimizar el uso de recursos.  4. *Pausa entre lotes*:
       - Introduce un breve retraso entre lotes para evitar sobrecargar el sistema, especialmente útil en entornos 
         con recursos limitados.

    5. *Manejo de errores*:
       - Captura excepciones generadas por columnas individuales y continúa procesando las demás, 
         asegurando que el proceso no se detenga por errores aislados.
       - Permite la interrupción manual mediante un KeyboardInterrupt (Ctrl + C), asegurando la limpieza de 
         recursos antes de finalizar.  

    Salida:
    -------
    - Muestra en pantalla los gráficos generados para cada columna y los cierra automáticamente para optimizar 
      el uso de memoria.
    - Imprime mensajes en la consola para informar sobre el progreso del análisis (lotes procesados, columnas 
      con errores, etc.).

    Uso recomendado:
    -----------------
    Esta función es ideal para realizar análisis exploratorio de datos (EDA) cuando:
    - Trabajas con un conjunto de datos con un gran número de características.
    - Necesitas automatizar la generación de gráficos para todas las columnas, optimizando el uso de recursos.
    - Quieres identificar patrones generales en la relación entre las características y la variable objetivo.

    Manejo de errores:
    ------------------
    - Si una columna no puede procesarse por cualquier motivo (como valores no válidos o errores inesperados), 
      la función imprime el error en consola y continúa con la siguiente columna.
    - Permite interrumpir el proceso manualmente mediante Ctrl + C, asegurando la limpieza de memoria al finalizar.
    """
    # Configurar estilo
    set_plot_style()
    
    # Preparar columnas
    columns = [col for col in df.columns if col != target_col]
    total_cols = len(columns)
    
    print(f"Iniciando análisis de {total_cols} características")
    
    try:
        for i in range(0, total_cols, batch_size):
            # Verificar memoria
            if psutil.virtual_memory().percent > memory_threshold:
                clear_memory()
                print("\nLimpiando memoria...")
                time.sleep(2)
            
            batch_cols = columns[i:i + batch_size]
            print(f"\nProcesando lote {i//batch_size + 1} de {(total_cols + batch_size - 1)//batch_size}")
            
            for col in batch_cols:
                try:
                    # Determinar tipo de variable
                    is_continuous = (
                        df[col].dtype in [float, int] and 
                        len(df[col].unique()) > 10
                    )
                    
                    # Crear gráfico
                    fig = plot_feature(
                        df=df,
                        col_name=col,
                        isContinuous=is_continuous,
                        target=target_col,
                        max_categories=15,    # Límite de categorías
                        sample_size=50000     # Límite de registros
                    )
                    
                    if fig is not None:
                        plt.show()
                        plt.close(fig)
                    
                    # Limpiar memoria
                    clear_memory()
                    
                except Exception as e:
                    print(f"Error procesando {col}: {str(e)}")
                    continue
            
            # Pausa entre lotes
            time.sleep(1)
        
        print("\nProceso completado exitosamente.")
        
    except KeyboardInterrupt:
        print("\nProceso interrumpido por el usuario.")
        clear_memory()
    
    finally:
        clear_memory()

# Ejecutar la visualización
plot_all_features(
    df=df_application_train,
    target_col='TARGET',
    batch_size=3,           # 3 gráficos por lote
    memory_threshold=85     # Limpiar memoria al 85%
)

Iniciando análisis de 121 características

Procesando lote 1 de 41

Procesando lote 2 de 41

Procesando lote 3 de 41

Procesando lote 4 de 41

Procesando lote 5 de 41

Procesando lote 6 de 41

Procesando lote 7 de 41

Procesando lote 8 de 41

Procesando lote 9 de 41

Procesando lote 10 de 41

list_var_continuous

['COMMONAREA_MEDI',
 'COMMONAREA_AVG',
 'COMMONAREA_MODE',
 'NONLIVINGAPARTMENTS_MODE',
 'NONLIVINGAPARTMENTS_AVG',
 'NONLIVINGAPARTMENTS_MEDI',
 'LIVINGAPARTMENTS_MODE',
 'LIVINGAPARTMENTS_AVG',
 'LIVINGAPARTMENTS_MEDI',
 'FLOORSMIN_AVG',
 'FLOORSMIN_MODE',
 'FLOORSMIN_MEDI',
 'YEARS_BUILD_MEDI',
 'YEARS_BUILD_MODE',
 'YEARS_BUILD_AVG',
 'OWN_CAR_AGE',
 'LANDAREA_MEDI',
 'LANDAREA_MODE',
 'LANDAREA_AVG',
 'BASEMENTAREA_MEDI',
 'BASEMENTAREA_AVG',
 'BASEMENTAREA_MODE',
 'EXT_SOURCE_1',
 'NONLIVINGAREA_MODE',
 'NONLIVINGAREA_AVG',
 'NONLIVINGAREA_MEDI',
 'ELEVATORS_MEDI',
 'ELEVATORS_AVG',
 'ELEVATORS_MODE',
 'APARTMENTS_MEDI',
 'APARTMENTS_AVG',
 'APARTMENTS_MODE',
 'ENTRANCES_MEDI',
 'ENTRANCES_AVG',
 'ENTRANCES_MODE',
 'LIVINGAREA_AVG',
 'LIVINGAREA_MODE',
 'LIVINGAREA_MEDI',
 'FLOORSMAX_MODE',
 'FLOORSMAX_MEDI',
 'FLOORSMAX_AVG',
 'YEARS_BEGINEXPLUATATION_MODE',
 'YEARS_BEGINEXPLUATATION_MEDI',
 'YEARS_BEGINEXPLUATATION_AVG',
 'TOTALAREA_MODE',
 'EXT_SOURCE_3',
 'AMT_REQ_CREDIT_BUREAU_HOUR',
 'AMT_REQ_CREDIT_BUREAU_DAY',
 'AMT_REQ_CREDIT_BUREAU_WEEK',
 'AMT_REQ_CREDIT_BUREAU_MON',
 'AMT_REQ_CREDIT_BUREAU_QRT',
 'AMT_REQ_CREDIT_BUREAU_YEAR',
 'OBS_30_CNT_SOCIAL_CIRCLE',
 'DEF_30_CNT_SOCIAL_CIRCLE',
 'OBS_60_CNT_SOCIAL_CIRCLE',
 'DEF_60_CNT_SOCIAL_CIRCLE',
 'EXT_SOURCE_2',
 'AMT_GOODS_PRICE',
 'AMT_ANNUITY',
 'CNT_FAM_MEMBERS',
 'DAYS_LAST_PHONE_CHANGE',
 'AMT_CREDIT',
 'AMT_INCOME_TOTAL',
 'DAYS_REGISTRATION',
 'REGION_POPULATION_RELATIVE']

f_aux.get_deviation_of_mean_perc(df_application_train, list_var_continuous, target='TARGET', multiplier=3)

f_aux.get_corr_matrix(dataset = df_application_train[list_var_continuous], 
                metodo='pearson', size_figure=[10,8])

0

corr = df_application_train[list_var_continuous].corr('pearson')
new_corr = corr.abs()
new_corr.loc[:,:] = np.tril(new_corr, k=-1) # below main lower triangle of an array
new_corr = new_corr.stack().to_frame('correlation').reset_index().sort_values(by='correlation', ascending=False)
new_corr[new_corr['correlation']>0.6]

list_var_continuous

['COMMONAREA_MEDI',
 'COMMONAREA_AVG',
 'COMMONAREA_MODE',
 'NONLIVINGAPARTMENTS_MODE',
 'NONLIVINGAPARTMENTS_AVG',
 'NONLIVINGAPARTMENTS_MEDI',
 'LIVINGAPARTMENTS_MODE',
 'LIVINGAPARTMENTS_AVG',
 'LIVINGAPARTMENTS_MEDI',
 'FLOORSMIN_AVG',
 'FLOORSMIN_MODE',
 'FLOORSMIN_MEDI',
 'YEARS_BUILD_MEDI',
 'YEARS_BUILD_MODE',
 'YEARS_BUILD_AVG',
 'OWN_CAR_AGE',
 'LANDAREA_MEDI',
 'LANDAREA_MODE',
 'LANDAREA_AVG',
 'BASEMENTAREA_MEDI',
 'BASEMENTAREA_AVG',
 'BASEMENTAREA_MODE',
 'EXT_SOURCE_1',
 'NONLIVINGAREA_MODE',
 'NONLIVINGAREA_AVG',
 'NONLIVINGAREA_MEDI',
 'ELEVATORS_MEDI',
 'ELEVATORS_AVG',
 'ELEVATORS_MODE',
 'APARTMENTS_MEDI',
 'APARTMENTS_AVG',
 'APARTMENTS_MODE',
 'ENTRANCES_MEDI',
 'ENTRANCES_AVG',
 'ENTRANCES_MODE',
 'LIVINGAREA_AVG',
 'LIVINGAREA_MODE',
 'LIVINGAREA_MEDI',
 'FLOORSMAX_MODE',
 'FLOORSMAX_MEDI',
 'FLOORSMAX_AVG',
 'YEARS_BEGINEXPLUATATION_MODE',
 'YEARS_BEGINEXPLUATATION_MEDI',
 'YEARS_BEGINEXPLUATATION_AVG',
 'TOTALAREA_MODE',
 'EXT_SOURCE_3',
 'AMT_REQ_CREDIT_BUREAU_HOUR',
 'AMT_REQ_CREDIT_BUREAU_DAY',
 'AMT_REQ_CREDIT_BUREAU_WEEK',
 'AMT_REQ_CREDIT_BUREAU_MON',
 'AMT_REQ_CREDIT_BUREAU_QRT',
 'AMT_REQ_CREDIT_BUREAU_YEAR',
 'OBS_30_CNT_SOCIAL_CIRCLE',
 'DEF_30_CNT_SOCIAL_CIRCLE',
 'OBS_60_CNT_SOCIAL_CIRCLE',
 'DEF_60_CNT_SOCIAL_CIRCLE',
 'EXT_SOURCE_2',
 'AMT_GOODS_PRICE',
 'AMT_ANNUITY',
 'CNT_FAM_MEMBERS',
 'DAYS_LAST_PHONE_CHANGE',
 'AMT_CREDIT',
 'AMT_INCOME_TOTAL',
 'DAYS_REGISTRATION',
 'REGION_POPULATION_RELATIVE']

f_aux.get_percent_null_values_target(df_application_train, list_var_continuous, target='TARGET')

# Verificar valores nulos y su proporción
null_summary = df_application_train[list_var_continuous].isnull().mean().sort_values(ascending=False)
print(null_summary)

# Visualizar la distribución de los valores nulos
plt.figure(figsize=(12, 8))
sns.heatmap(df_application_train[list_var_continuous].isnull(), cbar=False, cmap='viridis')
plt.title('Mapa de Calor de Valores Nulos en Variables Continuas')
plt.show()

COMMONAREA_MEDI                 0.700136
COMMONAREA_MODE                 0.700136
COMMONAREA_AVG                  0.700136
NONLIVINGAPARTMENTS_MODE        0.695583
NONLIVINGAPARTMENTS_MEDI        0.695583
NONLIVINGAPARTMENTS_AVG         0.695583
LIVINGAPARTMENTS_MODE           0.684754
LIVINGAPARTMENTS_AVG            0.684754
LIVINGAPARTMENTS_MEDI           0.684754
FLOORSMIN_AVG                   0.679783
FLOORSMIN_MODE                  0.679783
FLOORSMIN_MEDI                  0.679783
YEARS_BUILD_MEDI                0.666182
YEARS_BUILD_MODE                0.666182
YEARS_BUILD_AVG                 0.666182
OWN_CAR_AGE                     0.660515
LANDAREA_MODE                   0.595054
LANDAREA_AVG                    0.595054
LANDAREA_MEDI                   0.595054
BASEMENTAREA_MEDI               0.586615
BASEMENTAREA_AVG                0.586615
BASEMENTAREA_MODE               0.586615
EXT_SOURCE_1                    0.563234
NONLIVINGAREA_MODE              0.553059
NONLIVINGAREA_AVG               0.553059
NONLIVINGAREA_MEDI              0.553059
ELEVATORS_MEDI                  0.533946
ELEVATORS_AVG                   0.533946
ELEVATORS_MODE                  0.533946
APARTMENTS_MEDI                 0.508512
APARTMENTS_AVG                  0.508512
APARTMENTS_MODE                 0.508512
ENTRANCES_MODE                  0.504288
ENTRANCES_MEDI                  0.504288
ENTRANCES_AVG                   0.504288
LIVINGAREA_AVG                  0.502988
LIVINGAREA_MODE                 0.502988
LIVINGAREA_MEDI                 0.502988
FLOORSMAX_MODE                  0.498549
FLOORSMAX_MEDI                  0.498549
FLOORSMAX_AVG                   0.498549
YEARS_BEGINEXPLUATATION_MODE    0.488822
YEARS_BEGINEXPLUATATION_MEDI    0.488822
YEARS_BEGINEXPLUATATION_AVG     0.488822
TOTALAREA_MODE                  0.483671
EXT_SOURCE_3                    0.198302
AMT_REQ_CREDIT_BUREAU_YEAR      0.134979
AMT_REQ_CREDIT_BUREAU_QRT       0.134979
AMT_REQ_CREDIT_BUREAU_MON       0.134979
AMT_REQ_CREDIT_BUREAU_WEEK      0.134979
AMT_REQ_CREDIT_BUREAU_DAY       0.134979
AMT_REQ_CREDIT_BUREAU_HOUR      0.134979
OBS_30_CNT_SOCIAL_CIRCLE        0.003317
DEF_30_CNT_SOCIAL_CIRCLE        0.003317
OBS_60_CNT_SOCIAL_CIRCLE        0.003317
DEF_60_CNT_SOCIAL_CIRCLE        0.003317
EXT_SOURCE_2                    0.002183
AMT_GOODS_PRICE                 0.000890
AMT_ANNUITY                     0.000033
CNT_FAM_MEMBERS                 0.000004
DAYS_LAST_PHONE_CHANGE          0.000004
AMT_CREDIT                      0.000000
AMT_INCOME_TOTAL                0.000000
DAYS_REGISTRATION               0.000000
REGION_POPULATION_RELATIVE      0.000000
dtype: float64

# Suponiendo que df_application_train y df_application_test son tus DataFrames originales
df_application_input_train = df_application_train.copy()
df_application_input_test = df_application_test.copy()

# Definir la lista de variables continuas excluyendo la variable objetivo
list_var_continuous = [col for col in df_application_input_train.columns if df_application_input_train[col].dtype in ['float64', 'int64'] and col != 'TARGET']

# Definir las columnas que no deben ser tocadas (protegidas)
protected_columns = ['OWN_CAR_AGE', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3', 
                     'OBS_30_CNT_SOCIAL_CIRCLE', 'DEF_30_CNT_SOCIAL_CIRCLE', 
                     'OBS_60_CNT_SOCIAL_CIRCLE', 'DEF_60_CNT_SOCIAL_CIRCLE']

# Excluir estas columnas de la lista de imputación
list_vars_to_impute = [col for col in list_var_continuous if col not in protected_columns]

# Imputar valores nulos con -99 en las variables seleccionadas
df_application_input_train[list_vars_to_impute] = df_application_input_train[list_vars_to_impute].fillna(-99)
df_application_input_test[list_vars_to_impute] = df_application_input_test[list_vars_to_impute].fillna(-99)

# Verificar los resultados
print("Valores nulos en las columnas seleccionadas del conjunto de entrenamiento:")
print(df_application_input_train[list_vars_to_impute].isnull().sum())

print("Valores nulos en las columnas seleccionadas del conjunto de prueba:")
print(df_application_input_test[list_vars_to_impute].isnull().sum())

Valores nulos en las columnas seleccionadas del conjunto de entrenamiento:
COMMONAREA_MEDI                 0
COMMONAREA_AVG                  0
COMMONAREA_MODE                 0
NONLIVINGAPARTMENTS_MODE        0
NONLIVINGAPARTMENTS_AVG         0
NONLIVINGAPARTMENTS_MEDI        0
LIVINGAPARTMENTS_MODE           0
LIVINGAPARTMENTS_AVG            0
LIVINGAPARTMENTS_MEDI           0
FLOORSMIN_AVG                   0
FLOORSMIN_MODE                  0
FLOORSMIN_MEDI                  0
YEARS_BUILD_MEDI                0
YEARS_BUILD_MODE                0
YEARS_BUILD_AVG                 0
LANDAREA_MEDI                   0
LANDAREA_MODE                   0
LANDAREA_AVG                    0
BASEMENTAREA_MEDI               0
BASEMENTAREA_AVG                0
BASEMENTAREA_MODE               0
NONLIVINGAREA_MODE              0
NONLIVINGAREA_AVG               0
NONLIVINGAREA_MEDI              0
ELEVATORS_MEDI                  0
ELEVATORS_AVG                   0
ELEVATORS_MODE                  0
APARTMENTS_MEDI                 0
APARTMENTS_AVG                  0
APARTMENTS_MODE                 0
ENTRANCES_MEDI                  0
ENTRANCES_AVG                   0
ENTRANCES_MODE                  0
LIVINGAREA_AVG                  0
LIVINGAREA_MODE                 0
LIVINGAREA_MEDI                 0
FLOORSMAX_MODE                  0
FLOORSMAX_MEDI                  0
FLOORSMAX_AVG                   0
YEARS_BEGINEXPLUATATION_MODE    0
YEARS_BEGINEXPLUATATION_MEDI    0
YEARS_BEGINEXPLUATATION_AVG     0
TOTALAREA_MODE                  0
AMT_REQ_CREDIT_BUREAU_HOUR      0
AMT_REQ_CREDIT_BUREAU_DAY       0
AMT_REQ_CREDIT_BUREAU_WEEK      0
AMT_REQ_CREDIT_BUREAU_MON       0
AMT_REQ_CREDIT_BUREAU_QRT       0
AMT_REQ_CREDIT_BUREAU_YEAR      0
AMT_GOODS_PRICE                 0
AMT_ANNUITY                     0
CNT_FAM_MEMBERS                 0
DAYS_LAST_PHONE_CHANGE          0
AMT_CREDIT                      0
AMT_INCOME_TOTAL                0
DAYS_ID_PUBLISH                 0
DAYS_REGISTRATION               0
DAYS_EMPLOYED                   0
DAYS_BIRTH                      0
REGION_POPULATION_RELATIVE      0
SK_ID_CURR                      0
dtype: int64
Valores nulos en las columnas seleccionadas del conjunto de prueba:
COMMONAREA_MEDI                 0
COMMONAREA_AVG                  0
COMMONAREA_MODE                 0
NONLIVINGAPARTMENTS_MODE        0
NONLIVINGAPARTMENTS_AVG         0
NONLIVINGAPARTMENTS_MEDI        0
LIVINGAPARTMENTS_MODE           0
LIVINGAPARTMENTS_AVG            0
LIVINGAPARTMENTS_MEDI           0
FLOORSMIN_AVG                   0
FLOORSMIN_MODE                  0
FLOORSMIN_MEDI                  0
YEARS_BUILD_MEDI                0
YEARS_BUILD_MODE                0
YEARS_BUILD_AVG                 0
LANDAREA_MEDI                   0
LANDAREA_MODE                   0
LANDAREA_AVG                    0
BASEMENTAREA_MEDI               0
BASEMENTAREA_AVG                0
BASEMENTAREA_MODE               0
NONLIVINGAREA_MODE              0
NONLIVINGAREA_AVG               0
NONLIVINGAREA_MEDI              0
ELEVATORS_MEDI                  0
ELEVATORS_AVG                   0
ELEVATORS_MODE                  0
APARTMENTS_MEDI                 0
APARTMENTS_AVG                  0
APARTMENTS_MODE                 0
ENTRANCES_MEDI                  0
ENTRANCES_AVG                   0
ENTRANCES_MODE                  0
LIVINGAREA_AVG                  0
LIVINGAREA_MODE                 0
LIVINGAREA_MEDI                 0
FLOORSMAX_MODE                  0
FLOORSMAX_MEDI                  0
FLOORSMAX_AVG                   0
YEARS_BEGINEXPLUATATION_MODE    0
YEARS_BEGINEXPLUATATION_MEDI    0
YEARS_BEGINEXPLUATATION_AVG     0
TOTALAREA_MODE                  0
AMT_REQ_CREDIT_BUREAU_HOUR      0
AMT_REQ_CREDIT_BUREAU_DAY       0
AMT_REQ_CREDIT_BUREAU_WEEK      0
AMT_REQ_CREDIT_BUREAU_MON       0
AMT_REQ_CREDIT_BUREAU_QRT       0
AMT_REQ_CREDIT_BUREAU_YEAR      0
AMT_GOODS_PRICE                 0
AMT_ANNUITY                     0
CNT_FAM_MEMBERS                 0
DAYS_LAST_PHONE_CHANGE          0
AMT_CREDIT                      0
AMT_INCOME_TOTAL                0
DAYS_ID_PUBLISH                 0
DAYS_REGISTRATION               0
DAYS_EMPLOYED                   0
DAYS_BIRTH                      0
REGION_POPULATION_RELATIVE      0
SK_ID_CURR                      0
dtype: int64

list_vars_to_impute

['COMMONAREA_MEDI',
 'COMMONAREA_AVG',
 'COMMONAREA_MODE',
 'NONLIVINGAPARTMENTS_MODE',
 'NONLIVINGAPARTMENTS_AVG',
 'NONLIVINGAPARTMENTS_MEDI',
 'LIVINGAPARTMENTS_MODE',
 'LIVINGAPARTMENTS_AVG',
 'LIVINGAPARTMENTS_MEDI',
 'FLOORSMIN_AVG',
 'FLOORSMIN_MODE',
 'FLOORSMIN_MEDI',
 'YEARS_BUILD_MEDI',
 'YEARS_BUILD_MODE',
 'YEARS_BUILD_AVG',
 'LANDAREA_MEDI',
 'LANDAREA_MODE',
 'LANDAREA_AVG',
 'BASEMENTAREA_MEDI',
 'BASEMENTAREA_AVG',
 'BASEMENTAREA_MODE',
 'NONLIVINGAREA_MODE',
 'NONLIVINGAREA_AVG',
 'NONLIVINGAREA_MEDI',
 'ELEVATORS_MEDI',
 'ELEVATORS_AVG',
 'ELEVATORS_MODE',
 'APARTMENTS_MEDI',
 'APARTMENTS_AVG',
 'APARTMENTS_MODE',
 'ENTRANCES_MEDI',
 'ENTRANCES_AVG',
 'ENTRANCES_MODE',
 'LIVINGAREA_AVG',
 'LIVINGAREA_MODE',
 'LIVINGAREA_MEDI',
 'FLOORSMAX_MODE',
 'FLOORSMAX_MEDI',
 'FLOORSMAX_AVG',
 'YEARS_BEGINEXPLUATATION_MODE',
 'YEARS_BEGINEXPLUATATION_MEDI',
 'YEARS_BEGINEXPLUATATION_AVG',
 'TOTALAREA_MODE',
 'AMT_REQ_CREDIT_BUREAU_HOUR',
 'AMT_REQ_CREDIT_BUREAU_DAY',
 'AMT_REQ_CREDIT_BUREAU_WEEK',
 'AMT_REQ_CREDIT_BUREAU_MON',
 'AMT_REQ_CREDIT_BUREAU_QRT',
 'AMT_REQ_CREDIT_BUREAU_YEAR',
 'AMT_GOODS_PRICE',
 'AMT_ANNUITY',
 'CNT_FAM_MEMBERS',
 'DAYS_LAST_PHONE_CHANGE',
 'AMT_CREDIT',
 'AMT_INCOME_TOTAL',
 'DAYS_ID_PUBLISH',
 'DAYS_REGISTRATION',
 'DAYS_EMPLOYED',
 'DAYS_BIRTH',
 'REGION_POPULATION_RELATIVE',
 'SK_ID_CURR']

print(df_application_train.columns)

Index(['COMMONAREA_MEDI', 'COMMONAREA_AVG', 'COMMONAREA_MODE',
       'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAPARTMENTS_AVG',
       'NONLIVINGAPARTMENTS_MEDI', 'FONDKAPREMONT_MODE',
       'LIVINGAPARTMENTS_MODE', 'LIVINGAPARTMENTS_AVG',
       'LIVINGAPARTMENTS_MEDI',
       ...
       'DAYS_REGISTRATION', 'DAYS_EMPLOYED', 'DAYS_BIRTH',
       'REGION_POPULATION_RELATIVE', 'NAME_HOUSING_TYPE', 'NAME_FAMILY_STATUS',
       'NAME_EDUCATION_TYPE', 'NAME_INCOME_TYPE', 'SK_ID_CURR', 'TARGET'],
      dtype='object', length=122)

print(df_application_test.columns)

Index(['COMMONAREA_MEDI', 'COMMONAREA_AVG', 'COMMONAREA_MODE',
       'NONLIVINGAPARTMENTS_MODE', 'NONLIVINGAPARTMENTS_AVG',
       'NONLIVINGAPARTMENTS_MEDI', 'FONDKAPREMONT_MODE',
       'LIVINGAPARTMENTS_MODE', 'LIVINGAPARTMENTS_AVG',
       'LIVINGAPARTMENTS_MEDI',
       ...
       'DAYS_REGISTRATION', 'DAYS_EMPLOYED', 'DAYS_BIRTH',
       'REGION_POPULATION_RELATIVE', 'NAME_HOUSING_TYPE', 'NAME_FAMILY_STATUS',
       'NAME_EDUCATION_TYPE', 'NAME_INCOME_TYPE', 'SK_ID_CURR', 'TARGET'],
      dtype='object', length=122)

list_var_cat

['FONDKAPREMONT_MODE',
 'WALLSMATERIAL_MODE',
 'HOUSETYPE_MODE',
 'EMERGENCYSTATE_MODE',
 'OCCUPATION_TYPE',
 'NAME_TYPE_SUITE',
 'CNT_CHILDREN',
 'FLAG_DOCUMENT_8',
 'NAME_CONTRACT_TYPE',
 'CODE_GENDER',
 'FLAG_OWN_CAR',
 'FLAG_DOCUMENT_2',
 'FLAG_DOCUMENT_3',
 'FLAG_DOCUMENT_4',
 'FLAG_DOCUMENT_5',
 'FLAG_DOCUMENT_6',
 'FLAG_DOCUMENT_7',
 'FLAG_DOCUMENT_9',
 'FLAG_DOCUMENT_21',
 'FLAG_DOCUMENT_10',
 'FLAG_DOCUMENT_11',
 'FLAG_OWN_REALTY',
 'FLAG_DOCUMENT_13',
 'FLAG_DOCUMENT_14',
 'FLAG_DOCUMENT_15',
 'FLAG_DOCUMENT_16',
 'FLAG_DOCUMENT_17',
 'FLAG_DOCUMENT_18',
 'FLAG_DOCUMENT_19',
 'FLAG_DOCUMENT_20',
 'FLAG_DOCUMENT_12',
 'FLAG_PHONE',
 'LIVE_CITY_NOT_WORK_CITY',
 'REG_CITY_NOT_WORK_CITY',
 'TARGET',
 'REG_CITY_NOT_LIVE_CITY',
 'LIVE_REGION_NOT_WORK_REGION',
 'REG_REGION_NOT_WORK_REGION',
 'REG_REGION_NOT_LIVE_REGION',
 'HOUR_APPR_PROCESS_START',
 'WEEKDAY_APPR_PROCESS_START',
 'REGION_RATING_CLIENT_W_CITY',
 'REGION_RATING_CLIENT',
 'FLAG_EMAIL',
 'FLAG_CONT_MOBILE',
 'ORGANIZATION_TYPE',
 'FLAG_WORK_PHONE',
 'FLAG_EMP_PHONE',
 'FLAG_MOBIL',
 'NAME_HOUSING_TYPE',
 'NAME_FAMILY_STATUS',
 'NAME_EDUCATION_TYPE',
 'NAME_INCOME_TYPE']

def cramers_v(x, y):
    """ Calcular el coeficiente V de Cramer para la asociación entre dos variables categóricas """
    # Remover valores nulos
    x = x.dropna()
    y = y.dropna()
    
    # Solo calcular si ambas variables tienen datos
    if x.empty or y.empty:
        return np.nan
    
    confusion_matrix = pd.crosstab(x, y)
    if confusion_matrix.shape[0] == 1 or confusion_matrix.shape[1] == 1:
        return np.nan
    
    chi2 = chi2_contingency(confusion_matrix)[0]
    n = confusion_matrix.sum().sum()
    return np.sqrt(chi2 / (n * (min(confusion_matrix.shape) - 1)))

# Calcular V de Cramer por lotes de 2 en 2
lote_size = 2
for i in range(0, len(list_var_cat), lote_size):
    current_vars = list_var_cat[i:i + lote_size]
    cramers_v_matrix = pd.DataFrame(index=current_vars, columns=current_vars)
    for var1 in current_vars:
        for var2 in current_vars:
            if var1 == var2:
                cramers_v_matrix.loc[var1, var2] = 1.0
            else:
                # Combinar los datos y eliminar filas con NaN
                combined = pd.concat([df_application_input_train[var1], df_application_input_train[var2]], axis=1).dropna()
                if combined.empty or combined[var1].nunique() < 2 or combined[var2].nunique() < 2:
                    cramers_v_matrix.loc[var1, var2] = np.nan
                else:
                    cramers_v_matrix.loc[var1, var2] = cramers_v(combined[var1], combined[var2])

    # Convertir a tipo numérico
    cramers_v_matrix = cramers_v_matrix.astype(float)

    # Visualizar la matriz del lote actual
    print(f"Matriz de V de Cramer para el lote {i // lote_size + 1}:")
    print(cramers_v_matrix)

Matriz de V de Cramer para el lote 1:
                    FONDKAPREMONT_MODE  WALLSMATERIAL_MODE
FONDKAPREMONT_MODE             1.00000             0.10907
WALLSMATERIAL_MODE             0.10907             1.00000
Matriz de V de Cramer para el lote 2:
                     HOUSETYPE_MODE  EMERGENCYSTATE_MODE
HOUSETYPE_MODE             1.000000             0.036645
EMERGENCYSTATE_MODE        0.036645             1.000000
Matriz de V de Cramer para el lote 3:
                 OCCUPATION_TYPE  NAME_TYPE_SUITE
OCCUPATION_TYPE         1.000000         0.024306
NAME_TYPE_SUITE         0.024306         1.000000
Matriz de V de Cramer para el lote 4:
                 CNT_CHILDREN  FLAG_DOCUMENT_8
CNT_CHILDREN         1.000000         0.058121
FLAG_DOCUMENT_8      0.058121         1.000000
Matriz de V de Cramer para el lote 5:
                    NAME_CONTRACT_TYPE  CODE_GENDER
NAME_CONTRACT_TYPE            1.000000     0.014459
CODE_GENDER                   0.014459     1.000000
Matriz de V de Cramer para el lote 6:
                 FLAG_OWN_CAR  FLAG_DOCUMENT_2
FLAG_OWN_CAR         1.000000         0.000141
FLAG_DOCUMENT_2      0.000141         1.000000
Matriz de V de Cramer para el lote 7:
                 FLAG_DOCUMENT_3  FLAG_DOCUMENT_4
FLAG_DOCUMENT_3         1.000000         0.013614
FLAG_DOCUMENT_4         0.013614         1.000000
Matriz de V de Cramer para el lote 8:
                 FLAG_DOCUMENT_5  FLAG_DOCUMENT_6
FLAG_DOCUMENT_5         1.000000         0.038315
FLAG_DOCUMENT_6         0.038315         1.000000
Matriz de V de Cramer para el lote 9:
                 FLAG_DOCUMENT_7  FLAG_DOCUMENT_9
FLAG_DOCUMENT_7              1.0              0.0
FLAG_DOCUMENT_9              0.0              1.0
Matriz de V de Cramer para el lote 10:
                  FLAG_DOCUMENT_21  FLAG_DOCUMENT_10
FLAG_DOCUMENT_21               1.0               0.0
FLAG_DOCUMENT_10               0.0               1.0
Matriz de V de Cramer para el lote 11:
                  FLAG_DOCUMENT_11  FLAG_OWN_REALTY
FLAG_DOCUMENT_11          1.000000         0.033097
FLAG_OWN_REALTY           0.033097         1.000000
Matriz de V de Cramer para el lote 12:
                  FLAG_DOCUMENT_13  FLAG_DOCUMENT_14
FLAG_DOCUMENT_13          1.000000          0.002598
FLAG_DOCUMENT_14          0.002598          1.000000
Matriz de V de Cramer para el lote 13:
                  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16
FLAG_DOCUMENT_15          1.000000          0.002872
FLAG_DOCUMENT_16          0.002872          1.000000
Matriz de V de Cramer para el lote 14:
                  FLAG_DOCUMENT_17  FLAG_DOCUMENT_18
FLAG_DOCUMENT_17          1.000000          0.000088
FLAG_DOCUMENT_18          0.000088          1.000000
Matriz de V de Cramer para el lote 15:
                  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20
FLAG_DOCUMENT_19          1.000000          0.038168
FLAG_DOCUMENT_20          0.038168          1.000000
Matriz de V de Cramer para el lote 16:
                  FLAG_DOCUMENT_12  FLAG_PHONE
FLAG_DOCUMENT_12          1.000000    0.000196
FLAG_PHONE                0.000196    1.000000
Matriz de V de Cramer para el lote 17:
                         LIVE_CITY_NOT_WORK_CITY  REG_CITY_NOT_WORK_CITY
LIVE_CITY_NOT_WORK_CITY                 1.000000                0.825563
REG_CITY_NOT_WORK_CITY                  0.825563                1.000000
Matriz de V de Cramer para el lote 18:
                          TARGET  REG_CITY_NOT_LIVE_CITY
TARGET                  1.000000                0.043862
REG_CITY_NOT_LIVE_CITY  0.043862                1.000000
Matriz de V de Cramer para el lote 19:
                             LIVE_REGION_NOT_WORK_REGION  \
LIVE_REGION_NOT_WORK_REGION                     1.000000   
REG_REGION_NOT_WORK_REGION                      0.862185   

                             REG_REGION_NOT_WORK_REGION  
LIVE_REGION_NOT_WORK_REGION                    0.862185  
REG_REGION_NOT_WORK_REGION                     1.000000  
Matriz de V de Cramer para el lote 20:
                            REG_REGION_NOT_LIVE_REGION  \
REG_REGION_NOT_LIVE_REGION                    1.000000   
HOUR_APPR_PROCESS_START                       0.062793   

                            HOUR_APPR_PROCESS_START  
REG_REGION_NOT_LIVE_REGION                 0.062793  
HOUR_APPR_PROCESS_START                    1.000000  
Matriz de V de Cramer para el lote 21:
                             WEEKDAY_APPR_PROCESS_START  \
WEEKDAY_APPR_PROCESS_START                     1.000000   
REGION_RATING_CLIENT_W_CITY                    0.018289   

                             REGION_RATING_CLIENT_W_CITY  
WEEKDAY_APPR_PROCESS_START                      0.018289  
REGION_RATING_CLIENT_W_CITY                     1.000000  
Matriz de V de Cramer para el lote 22:
                      REGION_RATING_CLIENT  FLAG_EMAIL
REGION_RATING_CLIENT              1.000000    0.066095
FLAG_EMAIL                        0.066095    1.000000
Matriz de V de Cramer para el lote 23:
                   FLAG_CONT_MOBILE  ORGANIZATION_TYPE
FLAG_CONT_MOBILE           1.000000           0.025799
ORGANIZATION_TYPE          0.025799           1.000000
Matriz de V de Cramer para el lote 24:
                 FLAG_WORK_PHONE  FLAG_EMP_PHONE
FLAG_WORK_PHONE         1.000000        0.234059
FLAG_EMP_PHONE          0.234059        1.000000
Matriz de V de Cramer para el lote 25:
                   FLAG_MOBIL  NAME_HOUSING_TYPE
FLAG_MOBIL           1.000000           0.000717
NAME_HOUSING_TYPE    0.000717           1.000000
Matriz de V de Cramer para el lote 26:
                     NAME_FAMILY_STATUS  NAME_EDUCATION_TYPE
NAME_FAMILY_STATUS             1.000000             0.053392
NAME_EDUCATION_TYPE            0.053392             1.000000
Matriz de V de Cramer para el lote 27:
                  NAME_INCOME_TYPE
NAME_INCOME_TYPE               1.0

# Mostrar los primeros valores de las columnas categóricas
for col in list_var_cat:
    print(f"Valores únicos en la columna '{col}':")
    print(df_application_input_train[col].unique())
    print("\n")

Valores únicos en la columna 'FONDKAPREMONT_MODE':
['reg oper account', NaN, 'reg oper spec account', 'org spec account', 'not specified']
Categories (4, object): ['reg oper account', 'reg oper spec account', 'org spec account', 'not specified']


Valores únicos en la columna 'WALLSMATERIAL_MODE':
['Panel', NaN, 'Stone, brick', 'Others', 'Wooden', 'Mixed', 'Block', 'Monolithic']
Categories (7, object): ['Panel', 'Stone, brick', 'Others', 'Wooden', 'Mixed', 'Block', 'Monolithic']


Valores únicos en la columna 'HOUSETYPE_MODE':
['block of flats', NaN, 'specific housing', 'terraced house']
Categories (3, object): ['block of flats', 'specific housing', 'terraced house']


Valores únicos en la columna 'EMERGENCYSTATE_MODE':
['No', NaN, 'Yes']
Categories (2, object): ['No', 'Yes']


Valores únicos en la columna 'OCCUPATION_TYPE':
['Laborers', NaN, 'Drivers', 'Cleaning staff', 'Sales staff', ..., 'Realty agents', 'Private service staff', 'HR staff', 'IT staff', 'Secretaries']
Length: 19
Categories (18, object): ['Laborers', 'Drivers', 'Cleaning staff', 'Sales staff', ..., 'Private service staff', 'HR staff', 'IT staff', 'Secretaries']


Valores únicos en la columna 'NAME_TYPE_SUITE':
['Unaccompanied', 'Spouse, partner', 'Family', 'Children', 'Other_B', 'Other_A', NaN, 'Group of people']
Categories (7, object): ['Unaccompanied', 'Spouse, partner', 'Family', 'Children', 'Other_B', 'Other_A', 'Group of people']


Valores únicos en la columna 'CNT_CHILDREN':
[0, 1, 2, 3, 4, ..., 8, 7, 19, 9, 10]
Length: 14
Categories (14, int64): [0, 1, 2, 3, ..., 7, 19, 9, 10]


Valores únicos en la columna 'FLAG_DOCUMENT_8':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'NAME_CONTRACT_TYPE':
['Cash loans', 'Revolving loans']
Categories (2, object): ['Cash loans', 'Revolving loans']


Valores únicos en la columna 'CODE_GENDER':
['M', 'F', 'XNA']
Categories (3, object): ['M', 'F', 'XNA']


Valores únicos en la columna 'FLAG_OWN_CAR':
['Y', 'N']
Categories (2, object): ['Y', 'N']


Valores únicos en la columna 'FLAG_DOCUMENT_2':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_3':
[1, 0]
Categories (2, int64): [1, 0]


Valores únicos en la columna 'FLAG_DOCUMENT_4':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_5':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_6':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_7':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_9':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_21':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_10':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_11':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_OWN_REALTY':
['Y', 'N']
Categories (2, object): ['Y', 'N']


Valores únicos en la columna 'FLAG_DOCUMENT_13':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_14':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_15':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_16':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_17':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_18':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_19':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_20':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_DOCUMENT_12':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_PHONE':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'LIVE_CITY_NOT_WORK_CITY':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'REG_CITY_NOT_WORK_CITY':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'TARGET':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'REG_CITY_NOT_LIVE_CITY':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'LIVE_REGION_NOT_WORK_REGION':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'REG_REGION_NOT_WORK_REGION':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'REG_REGION_NOT_LIVE_REGION':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'HOUR_APPR_PROCESS_START':
[9, 11, 13, 17, 10, ..., 21, 23, 22, 1, 0]
Length: 24
Categories (24, int64): [9, 11, 13, 17, ..., 23, 22, 1, 0]


Valores únicos en la columna 'WEEKDAY_APPR_PROCESS_START':
['MONDAY', 'TUESDAY', 'THURSDAY', 'WEDNESDAY', 'FRIDAY', 'SATURDAY', 'SUNDAY']
Categories (7, object): ['MONDAY', 'TUESDAY', 'THURSDAY', 'WEDNESDAY', 'FRIDAY', 'SATURDAY', 'SUNDAY']


Valores únicos en la columna 'REGION_RATING_CLIENT_W_CITY':
[2, 3, 1]
Categories (3, int64): [2, 3, 1]


Valores únicos en la columna 'REGION_RATING_CLIENT':
[2, 3, 1]
Categories (3, int64): [2, 3, 1]


Valores únicos en la columna 'FLAG_EMAIL':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_CONT_MOBILE':
[1, 0]
Categories (2, int64): [1, 0]


Valores únicos en la columna 'ORGANIZATION_TYPE':
['Business Entity Type 3', 'Business Entity Type 1', 'Construction', 'Self-employed', 'XNA', ..., 'Cleaning', 'Industry: type 13', 'Industry: type 6', 'Trade: type 5', 'Religion']
Length: 58
Categories (58, object): ['Business Entity Type 3', 'Business Entity Type 1', 'Construction', 'Self-employed', ..., 'Industry: type 13', 'Industry: type 6', 'Trade: type 5', 'Religion']


Valores únicos en la columna 'FLAG_WORK_PHONE':
[0, 1]
Categories (2, int64): [0, 1]


Valores únicos en la columna 'FLAG_EMP_PHONE':
[1, 0]
Categories (2, int64): [1, 0]


Valores únicos en la columna 'FLAG_MOBIL':
[1, 0]
Categories (2, int64): [1, 0]


Valores únicos en la columna 'NAME_HOUSING_TYPE':
['House / apartment', 'With parents', 'Office apartment', 'Rented apartment', 'Municipal apartment', 'Co-op apartment']
Categories (6, object): ['House / apartment', 'With parents', 'Office apartment', 'Rented apartment', 'Municipal apartment', 'Co-op apartment']


Valores únicos en la columna 'NAME_FAMILY_STATUS':
['Married', 'Single / not married', 'Widow', 'Civil marriage', 'Separated', 'Unknown']
Categories (6, object): ['Married', 'Single / not married', 'Widow', 'Civil marriage', 'Separated', 'Unknown']


Valores únicos en la columna 'NAME_EDUCATION_TYPE':
['Higher education', 'Secondary / secondary special', 'Incomplete higher', 'Lower secondary', 'Academic degree']
Categories (5, object): ['Higher education', 'Secondary / secondary special', 'Incomplete higher', 'Lower secondary', 'Academic degree']


Valores únicos en la columna 'NAME_INCOME_TYPE':
['Commercial associate', 'Working', 'Pensioner', 'State servant', 'Student', 'Businessman', 'Maternity leave', 'Unemployed']
Categories (8, object): ['Commercial associate', 'Working', 'Pensioner', 'State servant', 'Student', 'Businessman', 'Maternity leave', 'Unemployed']

# Verificar la cantidad de valores nulos en las variables categóricas
print("Valores nulos en las variables categóricas:")
print(df_application_input_train[list_var_cat].isnull().sum())

# Verificar la cantidad de valores no nulos en las variables categóricas
print("Valores no nulos en las variables categóricas:")
print(df_application_input_train[list_var_cat].notnull().sum())

Valores nulos en las variables categóricas:
FONDKAPREMONT_MODE             168499
WALLSMATERIAL_MODE             125311
HOUSETYPE_MODE                 123650
EMERGENCYSTATE_MODE            116870
OCCUPATION_TYPE                 77303
NAME_TYPE_SUITE                  1035
CNT_CHILDREN                        0
FLAG_DOCUMENT_8                     0
NAME_CONTRACT_TYPE                  0
CODE_GENDER                         0
FLAG_OWN_CAR                        0
FLAG_DOCUMENT_2                     0
FLAG_DOCUMENT_3                     0
FLAG_DOCUMENT_4                     0
FLAG_DOCUMENT_5                     0
FLAG_DOCUMENT_6                     0
FLAG_DOCUMENT_7                     0
FLAG_DOCUMENT_9                     0
FLAG_DOCUMENT_21                    0
FLAG_DOCUMENT_10                    0
FLAG_DOCUMENT_11                    0
FLAG_OWN_REALTY                     0
FLAG_DOCUMENT_13                    0
FLAG_DOCUMENT_14                    0
FLAG_DOCUMENT_15                    0
FLAG_DOCUMENT_16                    0
FLAG_DOCUMENT_17                    0
FLAG_DOCUMENT_18                    0
FLAG_DOCUMENT_19                    0
FLAG_DOCUMENT_20                    0
FLAG_DOCUMENT_12                    0
FLAG_PHONE                          0
LIVE_CITY_NOT_WORK_CITY             0
REG_CITY_NOT_WORK_CITY              0
TARGET                              0
REG_CITY_NOT_LIVE_CITY              0
LIVE_REGION_NOT_WORK_REGION         0
REG_REGION_NOT_WORK_REGION          0
REG_REGION_NOT_LIVE_REGION          0
HOUR_APPR_PROCESS_START             0
WEEKDAY_APPR_PROCESS_START          0
REGION_RATING_CLIENT_W_CITY         0
REGION_RATING_CLIENT                0
FLAG_EMAIL                          0
FLAG_CONT_MOBILE                    0
ORGANIZATION_TYPE                   0
FLAG_WORK_PHONE                     0
FLAG_EMP_PHONE                      0
FLAG_MOBIL                          0
NAME_HOUSING_TYPE                   0
NAME_FAMILY_STATUS                  0
NAME_EDUCATION_TYPE                 0
NAME_INCOME_TYPE                    0
dtype: int64
Valores no nulos en las variables categóricas:
FONDKAPREMONT_MODE              77509
WALLSMATERIAL_MODE             120697
HOUSETYPE_MODE                 122358
EMERGENCYSTATE_MODE            129138
OCCUPATION_TYPE                168705
NAME_TYPE_SUITE                244973
CNT_CHILDREN                   246008
FLAG_DOCUMENT_8                246008
NAME_CONTRACT_TYPE             246008
CODE_GENDER                    246008
FLAG_OWN_CAR                   246008
FLAG_DOCUMENT_2                246008
FLAG_DOCUMENT_3                246008
FLAG_DOCUMENT_4                246008
FLAG_DOCUMENT_5                246008
FLAG_DOCUMENT_6                246008
FLAG_DOCUMENT_7                246008
FLAG_DOCUMENT_9                246008
FLAG_DOCUMENT_21               246008
FLAG_DOCUMENT_10               246008
FLAG_DOCUMENT_11               246008
FLAG_OWN_REALTY                246008
FLAG_DOCUMENT_13               246008
FLAG_DOCUMENT_14               246008
FLAG_DOCUMENT_15               246008
FLAG_DOCUMENT_16               246008
FLAG_DOCUMENT_17               246008
FLAG_DOCUMENT_18               246008
FLAG_DOCUMENT_19               246008
FLAG_DOCUMENT_20               246008
FLAG_DOCUMENT_12               246008
FLAG_PHONE                     246008
LIVE_CITY_NOT_WORK_CITY        246008
REG_CITY_NOT_WORK_CITY         246008
TARGET                         246008
REG_CITY_NOT_LIVE_CITY         246008
LIVE_REGION_NOT_WORK_REGION    246008
REG_REGION_NOT_WORK_REGION     246008
REG_REGION_NOT_LIVE_REGION     246008
HOUR_APPR_PROCESS_START        246008
WEEKDAY_APPR_PROCESS_START     246008
REGION_RATING_CLIENT_W_CITY    246008
REGION_RATING_CLIENT           246008
FLAG_EMAIL                     246008
FLAG_CONT_MOBILE               246008
ORGANIZATION_TYPE              246008
FLAG_WORK_PHONE                246008
FLAG_EMP_PHONE                 246008
FLAG_MOBIL                     246008
NAME_HOUSING_TYPE              246008
NAME_FAMILY_STATUS             246008
NAME_EDUCATION_TYPE            246008
NAME_INCOME_TYPE               246008
dtype: int64

# Verificar la cantidad de valores nulos en las variables categóricas
print("Valores nulos en las variables categóricas:")
print(df_application_input_test[list_var_cat].isnull().sum())

# Verificar la cantidad de valores no nulos en las variables categóricas
print("Valores no nulos en las variables categóricas:")
print(df_application_input_test[list_var_cat].notnull().sum())

Valores nulos en las variables categóricas:
FONDKAPREMONT_MODE             41796
WALLSMATERIAL_MODE             31030
HOUSETYPE_MODE                 30647
EMERGENCYSTATE_MODE            28885
OCCUPATION_TYPE                19088
NAME_TYPE_SUITE                  257
CNT_CHILDREN                       0
FLAG_DOCUMENT_8                    0
NAME_CONTRACT_TYPE                 0
CODE_GENDER                        0
FLAG_OWN_CAR                       0
FLAG_DOCUMENT_2                    0
FLAG_DOCUMENT_3                    0
FLAG_DOCUMENT_4                    0
FLAG_DOCUMENT_5                    0
FLAG_DOCUMENT_6                    0
FLAG_DOCUMENT_7                    0
FLAG_DOCUMENT_9                    0
FLAG_DOCUMENT_21                   0
FLAG_DOCUMENT_10                   0
FLAG_DOCUMENT_11                   0
FLAG_OWN_REALTY                    0
FLAG_DOCUMENT_13                   0
FLAG_DOCUMENT_14                   0
FLAG_DOCUMENT_15                   0
FLAG_DOCUMENT_16                   0
FLAG_DOCUMENT_17                   0
FLAG_DOCUMENT_18                   0
FLAG_DOCUMENT_19                   0
FLAG_DOCUMENT_20                   0
FLAG_DOCUMENT_12                   0
FLAG_PHONE                         0
LIVE_CITY_NOT_WORK_CITY            0
REG_CITY_NOT_WORK_CITY             0
TARGET                             0
REG_CITY_NOT_LIVE_CITY             0
LIVE_REGION_NOT_WORK_REGION        0
REG_REGION_NOT_WORK_REGION         0
REG_REGION_NOT_LIVE_REGION         0
HOUR_APPR_PROCESS_START            0
WEEKDAY_APPR_PROCESS_START         0
REGION_RATING_CLIENT_W_CITY        0
REGION_RATING_CLIENT               0
FLAG_EMAIL                         0
FLAG_CONT_MOBILE                   0
ORGANIZATION_TYPE                  0
FLAG_WORK_PHONE                    0
FLAG_EMP_PHONE                     0
FLAG_MOBIL                         0
NAME_HOUSING_TYPE                  0
NAME_FAMILY_STATUS                 0
NAME_EDUCATION_TYPE                0
NAME_INCOME_TYPE                   0
dtype: int64
Valores no nulos en las variables categóricas:
FONDKAPREMONT_MODE             19707
WALLSMATERIAL_MODE             30473
HOUSETYPE_MODE                 30856
EMERGENCYSTATE_MODE            32618
OCCUPATION_TYPE                42415
NAME_TYPE_SUITE                61246
CNT_CHILDREN                   61503
FLAG_DOCUMENT_8                61503
NAME_CONTRACT_TYPE             61503
CODE_GENDER                    61503
FLAG_OWN_CAR                   61503
FLAG_DOCUMENT_2                61503
FLAG_DOCUMENT_3                61503
FLAG_DOCUMENT_4                61503
FLAG_DOCUMENT_5                61503
FLAG_DOCUMENT_6                61503
FLAG_DOCUMENT_7                61503
FLAG_DOCUMENT_9                61503
FLAG_DOCUMENT_21               61503
FLAG_DOCUMENT_10               61503
FLAG_DOCUMENT_11               61503
FLAG_OWN_REALTY                61503
FLAG_DOCUMENT_13               61503
FLAG_DOCUMENT_14               61503
FLAG_DOCUMENT_15               61503
FLAG_DOCUMENT_16               61503
FLAG_DOCUMENT_17               61503
FLAG_DOCUMENT_18               61503
FLAG_DOCUMENT_19               61503
FLAG_DOCUMENT_20               61503
FLAG_DOCUMENT_12               61503
FLAG_PHONE                     61503
LIVE_CITY_NOT_WORK_CITY        61503
REG_CITY_NOT_WORK_CITY         61503
TARGET                         61503
REG_CITY_NOT_LIVE_CITY         61503
LIVE_REGION_NOT_WORK_REGION    61503
REG_REGION_NOT_WORK_REGION     61503
REG_REGION_NOT_LIVE_REGION     61503
HOUR_APPR_PROCESS_START        61503
WEEKDAY_APPR_PROCESS_START     61503
REGION_RATING_CLIENT_W_CITY    61503
REGION_RATING_CLIENT           61503
FLAG_EMAIL                     61503
FLAG_CONT_MOBILE               61503
ORGANIZATION_TYPE              61503
FLAG_WORK_PHONE                61503
FLAG_EMP_PHONE                 61503
FLAG_MOBIL                     61503
NAME_HOUSING_TYPE              61503
NAME_FAMILY_STATUS             61503
NAME_EDUCATION_TYPE            61503
NAME_INCOME_TYPE               61503
dtype: int64

# Sustituir valores nulos por "SIN VALOR" en variables categóricas
df_application_input_train[list_var_cat] = df_application_input_train[list_var_cat].astype("object").fillna("SIN VALOR").astype("category")
df_application_input_test[list_var_cat] = df_application_input_test[list_var_cat].astype("object").fillna("SIN VALOR").astype("category")

# Verificar los resultados
print("Valores nulos en las variables categóricas del conjunto de entrenamiento:")
print(df_application_input_train[list_var_cat].isnull().sum())

print("Valores nulos en las variables categóricas del conjunto de prueba:")
print(df_application_input_test[list_var_cat].isnull().sum())

Valores nulos en las variables categóricas del conjunto de entrenamiento:
FONDKAPREMONT_MODE             0
WALLSMATERIAL_MODE             0
HOUSETYPE_MODE                 0
EMERGENCYSTATE_MODE            0
OCCUPATION_TYPE                0
NAME_TYPE_SUITE                0
CNT_CHILDREN                   0
FLAG_DOCUMENT_8                0
NAME_CONTRACT_TYPE             0
CODE_GENDER                    0
FLAG_OWN_CAR                   0
FLAG_DOCUMENT_2                0
FLAG_DOCUMENT_3                0
FLAG_DOCUMENT_4                0
FLAG_DOCUMENT_5                0
FLAG_DOCUMENT_6                0
FLAG_DOCUMENT_7                0
FLAG_DOCUMENT_9                0
FLAG_DOCUMENT_21               0
FLAG_DOCUMENT_10               0
FLAG_DOCUMENT_11               0
FLAG_OWN_REALTY                0
FLAG_DOCUMENT_13               0
FLAG_DOCUMENT_14               0
FLAG_DOCUMENT_15               0
FLAG_DOCUMENT_16               0
FLAG_DOCUMENT_17               0
FLAG_DOCUMENT_18               0
FLAG_DOCUMENT_19               0
FLAG_DOCUMENT_20               0
FLAG_DOCUMENT_12               0
FLAG_PHONE                     0
LIVE_CITY_NOT_WORK_CITY        0
REG_CITY_NOT_WORK_CITY         0
TARGET                         0
REG_CITY_NOT_LIVE_CITY         0
LIVE_REGION_NOT_WORK_REGION    0
REG_REGION_NOT_WORK_REGION     0
REG_REGION_NOT_LIVE_REGION     0
HOUR_APPR_PROCESS_START        0
WEEKDAY_APPR_PROCESS_START     0
REGION_RATING_CLIENT_W_CITY    0
REGION_RATING_CLIENT           0
FLAG_EMAIL                     0
FLAG_CONT_MOBILE               0
ORGANIZATION_TYPE              0
FLAG_WORK_PHONE                0
FLAG_EMP_PHONE                 0
FLAG_MOBIL                     0
NAME_HOUSING_TYPE              0
NAME_FAMILY_STATUS             0
NAME_EDUCATION_TYPE            0
NAME_INCOME_TYPE               0
dtype: int64
Valores nulos en las variables categóricas del conjunto de prueba:
FONDKAPREMONT_MODE             0
WALLSMATERIAL_MODE             0
HOUSETYPE_MODE                 0
EMERGENCYSTATE_MODE            0
OCCUPATION_TYPE                0
NAME_TYPE_SUITE                0
CNT_CHILDREN                   0
FLAG_DOCUMENT_8                0
NAME_CONTRACT_TYPE             0
CODE_GENDER                    0
FLAG_OWN_CAR                   0
FLAG_DOCUMENT_2                0
FLAG_DOCUMENT_3                0
FLAG_DOCUMENT_4                0
FLAG_DOCUMENT_5                0
FLAG_DOCUMENT_6                0
FLAG_DOCUMENT_7                0
FLAG_DOCUMENT_9                0
FLAG_DOCUMENT_21               0
FLAG_DOCUMENT_10               0
FLAG_DOCUMENT_11               0
FLAG_OWN_REALTY                0
FLAG_DOCUMENT_13               0
FLAG_DOCUMENT_14               0
FLAG_DOCUMENT_15               0
FLAG_DOCUMENT_16               0
FLAG_DOCUMENT_17               0
FLAG_DOCUMENT_18               0
FLAG_DOCUMENT_19               0
FLAG_DOCUMENT_20               0
FLAG_DOCUMENT_12               0
FLAG_PHONE                     0
LIVE_CITY_NOT_WORK_CITY        0
REG_CITY_NOT_WORK_CITY         0
TARGET                         0
REG_CITY_NOT_LIVE_CITY         0
LIVE_REGION_NOT_WORK_REGION    0
REG_REGION_NOT_WORK_REGION     0
REG_REGION_NOT_LIVE_REGION     0
HOUR_APPR_PROCESS_START        0
WEEKDAY_APPR_PROCESS_START     0
REGION_RATING_CLIENT_W_CITY    0
REGION_RATING_CLIENT           0
FLAG_EMAIL                     0
FLAG_CONT_MOBILE               0
ORGANIZATION_TYPE              0
FLAG_WORK_PHONE                0
FLAG_EMP_PHONE                 0
FLAG_MOBIL                     0
NAME_HOUSING_TYPE              0
NAME_FAMILY_STATUS             0
NAME_EDUCATION_TYPE            0
NAME_INCOME_TYPE               0
dtype: int64

# Para comprobar

# Ver las primeras filas del conjunto de entrenamiento
print("Primeras filas del conjunto de entrenamiento:")
print(df_application_input_train[list_var_cat].head())

# Ver las primeras filas del conjunto de prueba
print("Primeras filas del conjunto de prueba:")
print(df_application_input_test[list_var_cat].head())

Primeras filas del conjunto de entrenamiento:
           FONDKAPREMONT_MODE WALLSMATERIAL_MODE  HOUSETYPE_MODE  \
141102       reg oper account              Panel  block of flats   
19825               SIN VALOR          SIN VALOR       SIN VALOR   
152561  reg oper spec account       Stone, brick  block of flats   
202298              SIN VALOR             Others  block of flats   
64580        reg oper account              Panel  block of flats   

       EMERGENCYSTATE_MODE OCCUPATION_TYPE  NAME_TYPE_SUITE CNT_CHILDREN  \
141102                  No        Laborers    Unaccompanied            0   
19825            SIN VALOR        Laborers  Spouse, partner            0   
152561                  No       SIN VALOR  Spouse, partner            1   
202298                  No         Drivers    Unaccompanied            0   
64580                   No       SIN VALOR    Unaccompanied            0   

       FLAG_DOCUMENT_8 NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \
141102               0         Cash loans           M            Y   
19825                0         Cash loans           M            Y   
152561               0         Cash loans           F            N   
202298               1         Cash loans           M            Y   
64580                0         Cash loans           F            Y   

       FLAG_DOCUMENT_2 FLAG_DOCUMENT_3 FLAG_DOCUMENT_4 FLAG_DOCUMENT_5  \
141102               0               1               0               0   
19825                0               1               0               0   
152561               0               1               0               0   
202298               0               0               0               0   
64580                0               0               0               0   

       FLAG_DOCUMENT_6 FLAG_DOCUMENT_7 FLAG_DOCUMENT_9 FLAG_DOCUMENT_21  \
141102               0               0               0                0   
19825                0               0               0                0   
152561               0               0               0                0   
202298               0               0               0                0   
64580                1               0               0                0   

       FLAG_DOCUMENT_10 FLAG_DOCUMENT_11 FLAG_OWN_REALTY FLAG_DOCUMENT_13  \
141102                0                0               Y                0   
19825                 0                0               N                0   
152561                0                0               Y                0   
202298                0                0               Y                0   
64580                 0                0               N                0   

       FLAG_DOCUMENT_14 FLAG_DOCUMENT_15 FLAG_DOCUMENT_16 FLAG_DOCUMENT_17  \
141102                0                0                0                0   
19825                 0                0                0                0   
152561                0                0                0                0   
202298                0                0                0                0   
64580                 0                0                0                0   

       FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_12  \
141102                0                0                0                0   
19825                 0                0                0                0   
152561                0                0                0                0   
202298                0                0                0                0   
64580                 0                0                0                0   

       FLAG_PHONE LIVE_CITY_NOT_WORK_CITY REG_CITY_NOT_WORK_CITY TARGET  \
141102          0                       0                      0      0   
19825           0                       0                      0      0   
152561          0                       0                      0      0   
202298          0                       0                      0      0   
64580           0                       0                      0      0   

       REG_CITY_NOT_LIVE_CITY LIVE_REGION_NOT_WORK_REGION  \
141102                      0                           0   
19825                       0                           0   
152561                      0                           0   
202298                      0                           0   
64580                       0                           0   

       REG_REGION_NOT_WORK_REGION REG_REGION_NOT_LIVE_REGION  \
141102                          0                          0   
19825                           0                          0   
152561                          0                          0   
202298                          0                          0   
64580                           0                          0   

       HOUR_APPR_PROCESS_START WEEKDAY_APPR_PROCESS_START  \
141102                       9                     MONDAY   
19825                       11                     MONDAY   
152561                      13                    TUESDAY   
202298                      17                     MONDAY   
64580                       10                   THURSDAY   

       REGION_RATING_CLIENT_W_CITY REGION_RATING_CLIENT FLAG_EMAIL  \
141102                           2                    2          0   
19825                            2                    2          0   
152561                           2                    2          0   
202298                           2                    2          0   
64580                            2                    2          0   

       FLAG_CONT_MOBILE       ORGANIZATION_TYPE FLAG_WORK_PHONE  \
141102                1  Business Entity Type 3               0   
19825                 1  Business Entity Type 1               0   
152561                1            Construction               0   
202298                1           Self-employed               0   
64580                 1                     XNA               0   

       FLAG_EMP_PHONE FLAG_MOBIL  NAME_HOUSING_TYPE    NAME_FAMILY_STATUS  \
141102              1          1  House / apartment               Married   
19825               1          1  House / apartment               Married   
152561              1          1  House / apartment               Married   
202298              1          1  House / apartment               Married   
64580               0          1  House / apartment  Single / not married   

                  NAME_EDUCATION_TYPE      NAME_INCOME_TYPE  
141102               Higher education  Commercial associate  
19825   Secondary / secondary special               Working  
152561  Secondary / secondary special               Working  
202298  Secondary / secondary special  Commercial associate  
64580   Secondary / secondary special             Pensioner  
Primeras filas del conjunto de prueba:
       FONDKAPREMONT_MODE WALLSMATERIAL_MODE  HOUSETYPE_MODE  \
241559      not specified              Mixed  block of flats   
258191   reg oper account       Stone, brick  block of flats   
157394          SIN VALOR       Stone, brick  block of flats   
97879           SIN VALOR          SIN VALOR       SIN VALOR   
142989          SIN VALOR          SIN VALOR       SIN VALOR   

       EMERGENCYSTATE_MODE OCCUPATION_TYPE NAME_TYPE_SUITE CNT_CHILDREN  \
241559                  No     Sales staff   Unaccompanied            0   
258191                  No        Laborers   Unaccompanied            0   
157394                  No      Core staff   Unaccompanied            0   
97879            SIN VALOR         Drivers   Unaccompanied            2   
142989           SIN VALOR     Accountants   Unaccompanied            0   

       FLAG_DOCUMENT_8 NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \
241559               0         Cash loans           F            Y   
258191               0         Cash loans           F            N   
157394               0         Cash loans           F            Y   
97879                0         Cash loans           M            Y   
142989               0         Cash loans           F            N   

       FLAG_DOCUMENT_2 FLAG_DOCUMENT_3 FLAG_DOCUMENT_4 FLAG_DOCUMENT_5  \
241559               0               1               0               0   
258191               0               1               0               0   
157394               0               1               0               0   
97879                0               0               0               0   
142989               0               1               0               0   

       FLAG_DOCUMENT_6 FLAG_DOCUMENT_7 FLAG_DOCUMENT_9 FLAG_DOCUMENT_21  \
241559               0               0               0                0   
258191               0               0               0                0   
157394               0               0               0                0   
97879                0               0               0                0   
142989               0               0               0                0   

       FLAG_DOCUMENT_10 FLAG_DOCUMENT_11 FLAG_OWN_REALTY FLAG_DOCUMENT_13  \
241559                0                0               N                0   
258191                0                0               Y                0   
157394                0                0               Y                0   
97879                 0                1               Y                0   
142989                0                0               Y                0   

       FLAG_DOCUMENT_14 FLAG_DOCUMENT_15 FLAG_DOCUMENT_16 FLAG_DOCUMENT_17  \
241559                0                0                0                0   
258191                0                0                0                0   
157394                0                0                0                0   
97879                 0                0                0                0   
142989                0                0                0                0   

       FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_12  \
241559                0                0                0                0   
258191                0                0                0                0   
157394                0                0                0                0   
97879                 0                0                0                0   
142989                0                0                0                0   

       FLAG_PHONE LIVE_CITY_NOT_WORK_CITY REG_CITY_NOT_WORK_CITY TARGET  \
241559          1                       0                      0      0   
258191          0                       0                      0      0   
157394          0                       0                      0      0   
97879           0                       0                      0      0   
142989          1                       0                      1      0   

       REG_CITY_NOT_LIVE_CITY LIVE_REGION_NOT_WORK_REGION  \
241559                      0                           0   
258191                      0                           0   
157394                      0                           0   
97879                       0                           0   
142989                      1                           0   

       REG_REGION_NOT_WORK_REGION REG_REGION_NOT_LIVE_REGION  \
241559                          0                          0   
258191                          0                          0   
157394                          0                          0   
97879                           0                          0   
142989                          0                          0   

       HOUR_APPR_PROCESS_START WEEKDAY_APPR_PROCESS_START  \
241559                      20                  WEDNESDAY   
258191                       7                    TUESDAY   
157394                      17                     MONDAY   
97879                       11                    TUESDAY   
142989                      13                    TUESDAY   

       REGION_RATING_CLIENT_W_CITY REGION_RATING_CLIENT FLAG_EMAIL  \
241559                           2                    2          0   
258191                           3                    3          0   
157394                           2                    2          0   
97879                            2                    2          0   
142989                           2                    2          0   

       FLAG_CONT_MOBILE  ORGANIZATION_TYPE FLAG_WORK_PHONE FLAG_EMP_PHONE  \
241559                1      Trade: type 7               1              1   
258191                1              Other               0              1   
157394                1           Medicine               0              1   
97879                 1              Other               1              1   
142989                1  Transport: type 4               0              1   

       FLAG_MOBIL  NAME_HOUSING_TYPE    NAME_FAMILY_STATUS  \
241559          1  House / apartment               Married   
258191          1  House / apartment               Married   
157394          1  House / apartment             Separated   
97879           1  House / apartment               Married   
142989          1  House / apartment  Single / not married   

                  NAME_EDUCATION_TYPE      NAME_INCOME_TYPE  
241559  Secondary / secondary special               Working  
258191  Secondary / secondary special               Working  
157394               Higher education  Commercial associate  
97879   Secondary / secondary special               Working  
142989               Higher education               Working

# Guardar los DataFrames preprocesados en archivos CSV
df_application_input_train.to_csv("../data/preprocessed_data/train_preprocessed_missing_outlier.csv", index=False)
df_application_input_test.to_csv("../data/preprocessed_data/test_preprocessed_missing_outlier.csv", index=False)

# Verificar los resultados de la operación
print("Archivos CSV creados y guardados con éxito.")

Archivos CSV creados y guardados con éxito.

print(df_application_input_train.shape, df_application_input_test.shape)

(246008, 122) (61503, 122)

	COMMONAREA_MEDI	COMMONAREA_AVG	COMMONAREA_MODE	NONLIVINGAPARTMENTS_MODE	NONLIVINGAPARTMENTS_AVG	NONLIVINGAPARTMENTS_MEDI	LIVINGAPARTMENTS_MODE	LIVINGAPARTMENTS_AVG	LIVINGAPARTMENTS_MEDI	FLOORSMIN_AVG	FLOORSMIN_MODE	FLOORSMIN_MEDI	YEARS_BUILD_MEDI	YEARS_BUILD_MODE	YEARS_BUILD_AVG	OWN_CAR_AGE	LANDAREA_MEDI	LANDAREA_MODE	LANDAREA_AVG	BASEMENTAREA_MEDI	BASEMENTAREA_AVG	BASEMENTAREA_MODE	EXT_SOURCE_1	NONLIVINGAREA_MODE	NONLIVINGAREA_AVG	NONLIVINGAREA_MEDI	ELEVATORS_MEDI	ELEVATORS_AVG	ELEVATORS_MODE	APARTMENTS_MEDI	APARTMENTS_AVG	APARTMENTS_MODE	ENTRANCES_MEDI	ENTRANCES_AVG	ENTRANCES_MODE	LIVINGAREA_AVG	LIVINGAREA_MODE	LIVINGAREA_MEDI	FLOORSMAX_MODE	FLOORSMAX_MEDI	FLOORSMAX_AVG	YEARS_BEGINEXPLUATATION_MODE	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BEGINEXPLUATATION_AVG	TOTALAREA_MODE	EXT_SOURCE_3	AMT_REQ_CREDIT_BUREAU_HOUR	AMT_REQ_CREDIT_BUREAU_DAY	AMT_REQ_CREDIT_BUREAU_WEEK	AMT_REQ_CREDIT_BUREAU_MON	AMT_REQ_CREDIT_BUREAU_QRT	AMT_REQ_CREDIT_BUREAU_YEAR	OBS_30_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	OBS_60_CNT_SOCIAL_CIRCLE	DEF_60_CNT_SOCIAL_CIRCLE	EXT_SOURCE_2	AMT_GOODS_PRICE	AMT_ANNUITY	CNT_FAM_MEMBERS	DAYS_LAST_PHONE_CHANGE	AMT_CREDIT	AMT_INCOME_TOTAL	DAYS_ID_PUBLISH	DAYS_REGISTRATION	DAYS_EMPLOYED	DAYS_BIRTH	REGION_POPULATION_RELATIVE	SK_ID_CURR
count	92646.000000	92646.000000	92646.000000	93997.000000	93997.000000	93997.000000	97312.000000	97312.000000	97312.000000	98869.000000	98869.000000	98869.000000	103023.000000	103023.000000	103023.000000	104582.000000	124921.000000	124921.000000	124921.000000	127568.000000	127568.000000	127568.000000	134133.000000	137829.000000	137829.000000	137829.000000	143620.000000	143620.000000	143620.000000	151450.000000	151450.00000	151450.000000	152683.000000	152683.000000	152683.000000	153161.000000	153161.000000	153161.000000	154491.000000	154491.000000	154491.000000	157504.000000	157504.000000	157504.000000	159080.000000	246546.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000	265992.000000	306490.000000	306490.000000	306490.000000	306490.000000	3.068510e+05	3.072330e+05	307499.000000	307509.000000	307510.000000	3.075110e+05	3.075110e+05	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000	307511.000000
mean	0.044595	0.044621	0.042553	0.008076	0.008809	0.008651	0.105645	0.100775	0.101954	0.231894	0.228058	0.231625	0.755746	0.759637	0.752471	12.061091	0.067169	0.064958	0.066333	0.087955	0.088442	0.087543	0.502130	0.027022	0.028358	0.028236	0.078078	0.078942	0.074490	0.117850	0.11744	0.114231	0.149213	0.149725	0.145193	0.107399	0.105975	0.108607	0.222315	0.225897	0.226282	0.977065	0.977752	0.977735	0.102547	0.510853	0.006402	0.007000	0.034362	0.267395	0.265474	1.899974	1.422245	0.143421	1.405292	0.100049	5.143927e-01	5.383962e+05	27108.573909	2.152665	-962.858788	5.990260e+05	1.687979e+05	-2994.202373	-4986.120328	63815.045904	-16036.995067	0.020868	278180.518577
std	0.076144	0.076036	0.074445	0.046276	0.047732	0.047415	0.097880	0.092576	0.093642	0.161380	0.161160	0.161934	0.112066	0.110111	0.113280	11.944812	0.082167	0.081750	0.081184	0.082179	0.082438	0.084307	0.211062	0.070254	0.069523	0.070166	0.134467	0.134576	0.132256	0.109076	0.10824	0.107936	0.100368	0.100049	0.100977	0.110565	0.111845	0.112260	0.143709	0.145067	0.144641	0.064575	0.059897	0.059223	0.107462	0.194844	0.083849	0.110757	0.204685	0.916002	0.794056	1.869295	2.400989	0.446698	2.379803	0.362291	1.910602e-01	3.694465e+05	14493.737315	0.910682	826.808487	4.024908e+05	2.371231e+05	1509.450419	3522.886321	141275.766519	4363.988632	0.013831	102790.175348
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.014568	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000527	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.173617e-08	4.050000e+04	1615.500000	1.000000	-4292.000000	4.500000e+04	2.565000e+04	-7197.000000	-24672.000000	-17912.000000	-25229.000000	0.000290	100002.000000
25%	0.007900	0.007800	0.007200	0.000000	0.000000	0.000000	0.054200	0.050400	0.051300	0.083300	0.083300	0.083300	0.691400	0.699400	0.687200	5.000000	0.018700	0.016600	0.018700	0.043700	0.044200	0.040700	0.334007	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.058300	0.05770	0.052500	0.069000	0.069000	0.069000	0.045300	0.042700	0.045700	0.166700	0.166700	0.166700	0.976700	0.976700	0.976700	0.041200	0.370650	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.924574e-01	2.385000e+05	16524.000000	2.000000	-1570.000000	2.700000e+05	1.125000e+05	-4299.000000	-7479.500000	-2760.000000	-19682.000000	0.010006	189145.500000
50%	0.020800	0.021100	0.019000	0.000000	0.000000	0.000000	0.077100	0.075600	0.076100	0.208300	0.208300	0.208300	0.758500	0.764800	0.755200	9.000000	0.048700	0.045800	0.048100	0.075800	0.076300	0.074600	0.505998	0.001100	0.003600	0.003100	0.000000	0.000000	0.000000	0.086400	0.08760	0.084000	0.137900	0.137900	0.137900	0.074500	0.073100	0.074900	0.166700	0.166700	0.166700	0.981600	0.981600	0.981600	0.068800	0.535276	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	5.659614e-01	4.500000e+05	24903.000000	2.000000	-757.000000	5.135310e+05	1.471500e+05	-3254.000000	-4504.000000	-1213.000000	-15750.000000	0.018850	278202.000000
75%	0.051300	0.051500	0.049000	0.003900	0.003900	0.003900	0.131300	0.121000	0.123100	0.375000	0.375000	0.375000	0.825600	0.823600	0.823200	15.000000	0.086800	0.084100	0.085600	0.111600	0.112200	0.112400	0.675053	0.023100	0.027700	0.026600	0.120000	0.120000	0.120800	0.148900	0.14850	0.143900	0.206900	0.206900	0.206900	0.129900	0.125200	0.130300	0.333300	0.333300	0.333300	0.986600	0.986600	0.986600	0.127600	0.669057	0.000000	0.000000	0.000000	0.000000	0.000000	3.000000	2.000000	0.000000	2.000000	0.000000	6.636171e-01	6.795000e+05	34596.000000	3.000000	-274.000000	8.086500e+05	2.025000e+05	-1720.000000	-2010.000000	-289.000000	-12413.000000	0.028663	367142.500000
max	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	91.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.962693	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.896010	4.000000	9.000000	8.000000	27.000000	261.000000	25.000000	348.000000	34.000000	344.000000	24.000000	8.549997e-01	4.050000e+06	258025.500000	20.000000	0.000000	4.050000e+06	1.170000e+08	0.000000	0.000000	365243.000000	-7489.000000	0.072508	456255.000000

	0.0	1.0	variable	sum_outlier_values	porcentaje_sum_null_values
0	0.945588	0.054412	COMMONAREA_MEDI	1360	0.005528
1	0.946389	0.053611	COMMONAREA_AVG	1343	0.005459
2	0.941264	0.058736	COMMONAREA_MODE	1345	0.005467
3	0.927911	0.072089	NONLIVINGAPARTMENTS_MODE	541	0.002199
4	0.936953	0.063047	NONLIVINGAPARTMENTS_AVG	571	0.002321
5	0.936508	0.063492	NONLIVINGAPARTMENTS_MEDI	567	0.002305
6	0.945139	0.054861	LIVINGAPARTMENTS_MODE	1440	0.005853
7	0.948080	0.051920	LIVINGAPARTMENTS_AVG	1406	0.005715
8	0.947146	0.052854	LIVINGAPARTMENTS_MEDI	1419	0.005768
9	0.962343	0.037657	FLOORSMIN_AVG	478	0.001943
10	0.965789	0.034211	FLOORSMIN_MODE	380	0.001545
11	0.963964	0.036036	FLOORSMIN_MEDI	444	0.001805
12	0.926501	0.073499	YEARS_BUILD_MEDI	966	0.003927
13	0.924431	0.075569	YEARS_BUILD_MODE	966	0.003927
14	0.925810	0.074190	YEARS_BUILD_AVG	957	0.003890
15	0.916981	0.083019	OWN_CAR_AGE	2650	0.010772
16	0.938453	0.061547	LANDAREA_MEDI	1706	0.006935
17	0.935126	0.064874	LANDAREA_MODE	1711	0.006955
18	0.936005	0.063995	LANDAREA_AVG	1672	0.006797
19	0.945980	0.054020	BASEMENTAREA_MEDI	1592	0.006471
20	0.946066	0.053934	BASEMENTAREA_AVG	1576	0.006406
21	0.946570	0.053430	BASEMENTAREA_MODE	1647	0.006695
22	0.947901	0.052099	NONLIVINGAREA_MODE	1977	0.008036
23	0.948731	0.051269	NONLIVINGAREA_AVG	1931	0.007849
24	0.948347	0.051653	NONLIVINGAREA_MEDI	1936	0.007870
25	0.955670	0.044330	ELEVATORS_MEDI	1940	0.007886
26	0.955339	0.044661	ELEVATORS_AVG	1948	0.007918
27	0.952114	0.047886	ELEVATORS_MODE	2673	0.010866
28	0.950833	0.049167	APARTMENTS_MEDI	2400	0.009756
29	0.950042	0.049958	APARTMENTS_AVG	2362	0.009601
30	0.949583	0.050417	APARTMENTS_MODE	2400	0.009756
31	0.941176	0.058824	ENTRANCES_MEDI	1785	0.007256
32	0.941441	0.058559	ENTRANCES_AVG	1776	0.007219
33	0.942490	0.057510	ENTRANCES_MODE	2104	0.008553
34	0.950137	0.049863	LIVINGAREA_AVG	2547	0.010353
35	0.948766	0.051234	LIVINGAREA_MODE	2674	0.010870
36	0.950973	0.049027	LIVINGAREA_MEDI	2570	0.010447
37	0.959009	0.040991	FLOORSMAX_MODE	2098	0.008528
38	0.957182	0.042818	FLOORSMAX_MEDI	2172	0.008829
39	0.957129	0.042871	FLOORSMAX_AVG	2076	0.008439
40	0.896869	0.103131	YEARS_BEGINEXPLUATATION_MODE	543	0.002207
41	0.892100	0.107900	YEARS_BEGINEXPLUATATION_MEDI	519	0.002110
42	0.898551	0.101449	YEARS_BEGINEXPLUATATION_AVG	552	0.002244
43	0.956473	0.043527	TOTALAREA_MODE	2665	0.010833
44	0.916987	0.083013	AMT_REQ_CREDIT_BUREAU_HOUR	1301	0.005288
45	0.907313	0.092687	AMT_REQ_CREDIT_BUREAU_DAY	1176	0.004780
46	0.921430	0.078570	AMT_REQ_CREDIT_BUREAU_WEEK	6771	0.027523
47	0.947511	0.052489	AMT_REQ_CREDIT_BUREAU_MON	2591	0.010532
48	0.914641	0.085359	AMT_REQ_CREDIT_BUREAU_QRT	1851	0.007524
49	0.905410	0.094590	AMT_REQ_CREDIT_BUREAU_YEAR	2717	0.011044
50	0.910855	0.089145	OBS_30_CNT_SOCIAL_CIRCLE	4947	0.020109
51	0.881458	0.118542	DEF_30_CNT_SOCIAL_CIRCLE	5458	0.022186
52	0.911000	0.089000	OBS_60_CNT_SOCIAL_CIRCLE	4809	0.019548
53	0.874166	0.125834	DEF_60_CNT_SOCIAL_CIRCLE	3147	0.012792
54	0.960048	0.039952	AMT_GOODS_PRICE	3304	0.013430
55	0.961881	0.038119	AMT_ANNUITY	2361	0.009597
56	0.898971	0.101029	CNT_FAM_MEMBERS	3207	0.013036
57	0.962227	0.037773	DAYS_LAST_PHONE_CHANGE	503	0.002045
58	0.958061	0.041939	AMT_CREDIT	2599	0.010565
59	0.936937	0.063063	AMT_INCOME_TOTAL	222	0.000902
60	0.957411	0.042589	DAYS_REGISTRATION	587	0.002386
61	0.958943	0.041057	REGION_POPULATION_RELATIVE	6698	0.027227

Valores missing, outlier y correlaciones¶

Importación de liberías¶

Funciones¶

Lectura de datos del preprocesado inicial¶

Separación en train y test estratificado¶

Visualización descriptiva de los datos¶

Distribución del resto de variables¶

Análisis de los gráficos¶

- Tratamiento de las variables continuas¶

Tratamiento de outliers¶

Distribución entre clases:¶

Valores Atípicos (Outliers):¶

Porcentaje de Valores Nulos:¶

Variables Clave para el Riesgo:¶

Correlaciones¶

Análisis de la matriz de correlación¶

Variables con correlación extremadamente alta (> 0.99):¶

Variables de medidas promedio y mediana (AVG y MEDI):¶

Relación entre área y otras variables estructurales:¶

Total Area vs. Componentes individuales:¶

Posible problema de multicolinealidad:¶

Tratamiento de valores nulos¶

Técnicas para tratamiento de valores nulos¶

Opción 0:¶

Opción 1:¶

Opción 2:¶

Justificación¶

¿Por qué imputar en ambos conjuntos (train y test)?¶

- Tratamiento de las variables categoricas¶

Coeficiente V de Cramer¶

Principales resultados:¶

Sustitución de valores nulos en variables categóricas¶

Guardado de las tablas¶

	nulos_columnas	porcentaje_columnas
COMMONAREA_MEDI	172239	0.700136
COMMONAREA_MODE	172239	0.700136
COMMONAREA_AVG	172239	0.700136
NONLIVINGAPARTMENTS_MODE	171119	0.695583
NONLIVINGAPARTMENTS_MEDI	171119	0.695583
NONLIVINGAPARTMENTS_AVG	171119	0.695583
FONDKAPREMONT_MODE	168499	0.684933
LIVINGAPARTMENTS_AVG	168455	0.684754
LIVINGAPARTMENTS_MEDI	168455	0.684754
LIVINGAPARTMENTS_MODE	168455	0.684754
FLOORSMIN_AVG	167232	0.679783
FLOORSMIN_MODE	167232	0.679783
FLOORSMIN_MEDI	167232	0.679783
YEARS_BUILD_MODE	163886	0.666182
YEARS_BUILD_AVG	163886	0.666182
YEARS_BUILD_MEDI	163886	0.666182
OWN_CAR_AGE	162492	0.660515
LANDAREA_MODE	146388	0.595054
LANDAREA_AVG	146388	0.595054
LANDAREA_MEDI	146388	0.595054
BASEMENTAREA_MEDI	144312	0.586615
BASEMENTAREA_AVG	144312	0.586615
BASEMENTAREA_MODE	144312	0.586615
EXT_SOURCE_1	138560	0.563234
NONLIVINGAREA_MODE	136057	0.553059
NONLIVINGAREA_AVG	136057	0.553059
NONLIVINGAREA_MEDI	136057	0.553059
ELEVATORS_AVG	131355	0.533946
ELEVATORS_MODE	131355	0.533946
ELEVATORS_MEDI	131355	0.533946
WALLSMATERIAL_MODE	125311	0.509378
APARTMENTS_MEDI	125098	0.508512
APARTMENTS_AVG	125098	0.508512
APARTMENTS_MODE	125098	0.508512
ENTRANCES_MEDI	124059	0.504288
ENTRANCES_AVG	124059	0.504288
ENTRANCES_MODE	124059	0.504288
LIVINGAREA_AVG	123739	0.502988
LIVINGAREA_MODE	123739	0.502988
LIVINGAREA_MEDI	123739	0.502988
HOUSETYPE_MODE	123650	0.502626
FLOORSMAX_MODE	122647	0.498549
FLOORSMAX_MEDI	122647	0.498549
FLOORSMAX_AVG	122647	0.498549
YEARS_BEGINEXPLUATATION_AVG	120254	0.488822
YEARS_BEGINEXPLUATATION_MODE	120254	0.488822
YEARS_BEGINEXPLUATATION_MEDI	120254	0.488822
TOTALAREA_MODE	118987	0.483671
EMERGENCYSTATE_MODE	116870	0.475066
OCCUPATION_TYPE	77303	0.314230
EXT_SOURCE_3	48784	0.198302
AMT_REQ_CREDIT_BUREAU_HOUR	33206	0.134979
AMT_REQ_CREDIT_BUREAU_DAY	33206	0.134979
AMT_REQ_CREDIT_BUREAU_WEEK	33206	0.134979
AMT_REQ_CREDIT_BUREAU_MON	33206	0.134979
AMT_REQ_CREDIT_BUREAU_QRT	33206	0.134979
AMT_REQ_CREDIT_BUREAU_YEAR	33206	0.134979
NAME_TYPE_SUITE	1035	0.004207
OBS_60_CNT_SOCIAL_CIRCLE	816	0.003317
DEF_60_CNT_SOCIAL_CIRCLE	816	0.003317
DEF_30_CNT_SOCIAL_CIRCLE	816	0.003317
OBS_30_CNT_SOCIAL_CIRCLE	816	0.003317
EXT_SOURCE_2	537	0.002183
AMT_GOODS_PRICE	219	0.000890
AMT_ANNUITY	8	0.000033
CNT_FAM_MEMBERS	1	0.000004
DAYS_LAST_PHONE_CHANGE	1	0.000004
HOUR_APPR_PROCESS_START	0	0.000000
FLAG_EMAIL	0	0.000000
REGION_RATING_CLIENT	0	0.000000
REGION_RATING_CLIENT_W_CITY	0	0.000000
WEEKDAY_APPR_PROCESS_START	0	0.000000
REG_CITY_NOT_LIVE_CITY	0	0.000000
REG_REGION_NOT_LIVE_REGION	0	0.000000
REG_REGION_NOT_WORK_REGION	0	0.000000
LIVE_REGION_NOT_WORK_REGION	0	0.000000
ORGANIZATION_TYPE	0	0.000000
REG_CITY_NOT_WORK_CITY	0	0.000000
FLAG_CONT_MOBILE	0	0.000000
FLAG_MOBIL	0	0.000000
FLAG_WORK_PHONE	0	0.000000
FLAG_EMP_PHONE	0	0.000000
FLAG_PHONE	0	0.000000
DAYS_ID_PUBLISH	0	0.000000
DAYS_REGISTRATION	0	0.000000
DAYS_EMPLOYED	0	0.000000
DAYS_BIRTH	0	0.000000
REGION_POPULATION_RELATIVE	0	0.000000
NAME_HOUSING_TYPE	0	0.000000
NAME_FAMILY_STATUS	0	0.000000
NAME_EDUCATION_TYPE	0	0.000000
NAME_INCOME_TYPE	0	0.000000
SK_ID_CURR	0	0.000000
LIVE_CITY_NOT_WORK_CITY	0	0.000000
FLAG_DOCUMENT_12	0	0.000000
AMT_INCOME_TOTAL	0	0.000000
FLAG_DOCUMENT_9	0	0.000000
CNT_CHILDREN	0	0.000000
FLAG_DOCUMENT_8	0	0.000000
NAME_CONTRACT_TYPE	0	0.000000
CODE_GENDER	0	0.000000
FLAG_OWN_CAR	0	0.000000
FLAG_DOCUMENT_2	0	0.000000
FLAG_DOCUMENT_3	0	0.000000
FLAG_DOCUMENT_4	0	0.000000
FLAG_DOCUMENT_5	0	0.000000
FLAG_DOCUMENT_6	0	0.000000
FLAG_DOCUMENT_7	0	0.000000
FLAG_DOCUMENT_21	0	0.000000
AMT_CREDIT	0	0.000000
FLAG_DOCUMENT_10	0	0.000000
FLAG_DOCUMENT_11	0	0.000000
FLAG_OWN_REALTY	0	0.000000
FLAG_DOCUMENT_13	0	0.000000
FLAG_DOCUMENT_14	0	0.000000
FLAG_DOCUMENT_15	0	0.000000
FLAG_DOCUMENT_16	0	0.000000
FLAG_DOCUMENT_17	0	0.000000
FLAG_DOCUMENT_18	0	0.000000
FLAG_DOCUMENT_19	0	0.000000
FLAG_DOCUMENT_20	0	0.000000
TARGET	0	0.000000

	level_0	level_1	correlation
3562	OBS_60_CNT_SOCIAL_CIRCLE	OBS_30_CNT_SOCIAL_CIRCLE	0.998525
922	YEARS_BUILD_AVG	YEARS_BUILD_MEDI	0.998408
724	FLOORSMIN_MEDI	FLOORSMIN_AVG	0.997172
2639	FLOORSMAX_AVG	FLOORSMAX_MEDI	0.996985
2177	ENTRANCES_AVG	ENTRANCES_MEDI	0.996877
1781	ELEVATORS_AVG	ELEVATORS_MEDI	0.996183
329	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAPARTMENTS_AVG	0.995871
2440	LIVINGAREA_MEDI	LIVINGAREA_AVG	0.995787
65	COMMONAREA_AVG	COMMONAREA_MEDI	0.995665
1319	BASEMENTAREA_AVG	BASEMENTAREA_MEDI	0.995040
1979	APARTMENTS_AVG	APARTMENTS_MEDI	0.994946
2837	YEARS_BEGINEXPLUATATION_AVG	YEARS_BEGINEXPLUATATION_MEDI	0.993873
527	LIVINGAPARTMENTS_MEDI	LIVINGAPARTMENTS_AVG	0.993505
1186	LANDAREA_AVG	LANDAREA_MEDI	0.991425
1649	NONLIVINGAREA_MEDI	NONLIVINGAREA_AVG	0.990395
857	YEARS_BUILD_MODE	YEARS_BUILD_MEDI	0.989499
923	YEARS_BUILD_AVG	YEARS_BUILD_MODE	0.989385
2573	FLOORSMAX_MEDI	FLOORSMAX_MODE	0.988307
725	FLOORSMIN_MEDI	FLOORSMIN_MODE	0.988150
4022	AMT_CREDIT	AMT_GOODS_PRICE	0.986973
2638	FLOORSMAX_AVG	FLOORSMAX_MODE	0.985728
659	FLOORSMIN_MODE	FLOORSMIN_AVG	0.985592
1846	ELEVATORS_MODE	ELEVATORS_MEDI	0.983095
2242	ENTRANCES_MODE	ENTRANCES_MEDI	0.981022
130	COMMONAREA_MODE	COMMONAREA_MEDI	0.980982
1121	LANDAREA_MODE	LANDAREA_MEDI	0.980668
1847	ELEVATORS_MODE	ELEVATORS_AVG	0.979216
1384	BASEMENTAREA_MODE	BASEMENTAREA_MEDI	0.978859
2243	ENTRANCES_MODE	ENTRANCES_AVG	0.978084
131	COMMONAREA_MODE	COMMONAREA_AVG	0.977639
2044	APARTMENTS_MODE	APARTMENTS_MEDI	0.977409
526	LIVINGAPARTMENTS_MEDI	LIVINGAPARTMENTS_MODE	0.976488
328	NONLIVINGAPARTMENTS_MEDI	NONLIVINGAPARTMENTS_MODE	0.976350
1648	NONLIVINGAREA_MEDI	NONLIVINGAREA_MODE	0.975192
1385	BASEMENTAREA_MODE	BASEMENTAREA_AVG	0.975115
2441	LIVINGAREA_MEDI	LIVINGAREA_MODE	0.975048
2045	APARTMENTS_MODE	APARTMENTS_AVG	0.973336
1187	LANDAREA_AVG	LANDAREA_MODE	0.973222
2375	LIVINGAREA_MODE	LIVINGAREA_AVG	0.972640
263	NONLIVINGAPARTMENTS_AVG	NONLIVINGAPARTMENTS_MODE	0.972186
2836	YEARS_BEGINEXPLUATATION_AVG	YEARS_BEGINEXPLUATATION_MODE	0.971791
461	LIVINGAPARTMENTS_AVG	LIVINGAPARTMENTS_MODE	0.970640
1583	NONLIVINGAREA_AVG	NONLIVINGAREA_MODE	0.965255
2771	YEARS_BEGINEXPLUATATION_MEDI	YEARS_BEGINEXPLUATATION_MODE	0.962514
1957	APARTMENTS_AVG	LIVINGAPARTMENTS_AVG	0.942412
1893	APARTMENTS_MEDI	LIVINGAPARTMENTS_MEDI	0.941058
1958	APARTMENTS_AVG	LIVINGAPARTMENTS_MEDI	0.940754
2021	APARTMENTS_MODE	LIVINGAPARTMENTS_MODE	0.937433
1892	APARTMENTS_MEDI	LIVINGAPARTMENTS_AVG	0.933741
1891	APARTMENTS_MEDI	LIVINGAPARTMENTS_MODE	0.931295
1956	APARTMENTS_AVG	LIVINGAPARTMENTS_MODE	0.929634
2895	TOTALAREA_MODE	LIVINGAREA_AVG	0.927090
2897	TOTALAREA_MODE	LIVINGAREA_MEDI	0.921248
2434	LIVINGAREA_MEDI	APARTMENTS_MEDI	0.915066
2023	APARTMENTS_MODE	LIVINGAPARTMENTS_MEDI	0.914695
2305	LIVINGAREA_AVG	APARTMENTS_AVG	0.912721
2304	LIVINGAREA_AVG	APARTMENTS_MEDI	0.911788
2435	LIVINGAREA_MEDI	APARTMENTS_AVG	0.911296
2371	LIVINGAREA_MODE	APARTMENTS_MODE	0.909635
2022	APARTMENTS_MODE	LIVINGAPARTMENTS_AVG	0.907987
2896	TOTALAREA_MODE	LIVINGAREA_MODE	0.901261
2369	LIVINGAREA_MODE	APARTMENTS_MEDI	0.895540
2436	LIVINGAREA_MEDI	APARTMENTS_MODE	0.893771
2890	TOTALAREA_MODE	APARTMENTS_AVG	0.892760
2370	LIVINGAREA_MODE	APARTMENTS_AVG	0.892735
2306	LIVINGAREA_AVG	APARTMENTS_MODE	0.890474
2889	TOTALAREA_MODE	APARTMENTS_MEDI	0.886804
2413	LIVINGAREA_MEDI	LIVINGAPARTMENTS_MEDI	0.883864
2283	LIVINGAREA_AVG	LIVINGAPARTMENTS_MEDI	0.882604
2282	LIVINGAREA_AVG	LIVINGAPARTMENTS_AVG	0.879748
2346	LIVINGAREA_MODE	LIVINGAPARTMENTS_MODE	0.878444
2412	LIVINGAREA_MEDI	LIVINGAPARTMENTS_AVG	0.877033
2411	LIVINGAREA_MEDI	LIVINGAPARTMENTS_MODE	0.873947
2281	LIVINGAREA_AVG	LIVINGAPARTMENTS_MODE	0.872807
2431	LIVINGAREA_MEDI	ELEVATORS_MEDI	0.869772
2302	LIVINGAREA_AVG	ELEVATORS_AVG	0.869226
2301	LIVINGAREA_AVG	ELEVATORS_MEDI	0.867280
2432	LIVINGAREA_MEDI	ELEVATORS_AVG	0.867040
2891	TOTALAREA_MODE	APARTMENTS_MODE	0.864217
3628	DEF_60_CNT_SOCIAL_CIRCLE	DEF_30_CNT_SOCIAL_CIRCLE	0.859851
2348	LIVINGAREA_MODE	LIVINGAPARTMENTS_MEDI	0.857836
2368	LIVINGAREA_MODE	ELEVATORS_MODE	0.857670
2433	LIVINGAREA_MEDI	ELEVATORS_MODE	0.857609
2303	LIVINGAREA_AVG	ELEVATORS_MODE	0.854463
2347	LIVINGAREA_MODE	LIVINGAPARTMENTS_AVG	0.851685
2867	TOTALAREA_MODE	LIVINGAPARTMENTS_AVG	0.850322
2868	TOTALAREA_MODE	LIVINGAPARTMENTS_MEDI	0.848769
2887	TOTALAREA_MODE	ELEVATORS_AVG	0.846746
2366	LIVINGAREA_MODE	ELEVATORS_MEDI	0.842724
2367	LIVINGAREA_MODE	ELEVATORS_AVG	0.840727
2886	TOTALAREA_MODE	ELEVATORS_MEDI	0.840222
1911	APARTMENTS_MEDI	ELEVATORS_MEDI	0.837901
1977	APARTMENTS_AVG	ELEVATORS_AVG	0.837233
2866	TOTALAREA_MODE	LIVINGAPARTMENTS_MODE	0.836881
1976	APARTMENTS_AVG	ELEVATORS_MEDI	0.835448
1912	APARTMENTS_MEDI	ELEVATORS_AVG	0.834955
2043	APARTMENTS_MODE	ELEVATORS_MODE	0.826669
1913	APARTMENTS_MEDI	ELEVATORS_MODE	0.826050
2888	TOTALAREA_MODE	ELEVATORS_MODE	0.823041
1978	APARTMENTS_AVG	ELEVATORS_MODE	0.822881
1698	ELEVATORS_MEDI	LIVINGAPARTMENTS_MEDI	0.814992
1763	ELEVATORS_AVG	LIVINGAPARTMENTS_MEDI	0.813249
1762	ELEVATORS_AVG	LIVINGAPARTMENTS_AVG	0.812103
1697	ELEVATORS_MEDI	LIVINGAPARTMENTS_AVG	0.809794
2041	APARTMENTS_MODE	ELEVATORS_MEDI	0.809667
1826	ELEVATORS_MODE	LIVINGAPARTMENTS_MODE	0.809361
2042	APARTMENTS_MODE	ELEVATORS_AVG	0.806875
1828	ELEVATORS_MODE	LIVINGAPARTMENTS_MEDI	0.801290
1696	ELEVATORS_MEDI	LIVINGAPARTMENTS_MODE	0.800061
1761	ELEVATORS_AVG	LIVINGAPARTMENTS_MODE	0.798216
1827	ELEVATORS_MODE	LIVINGAPARTMENTS_AVG	0.795661
3827	AMT_ANNUITY	AMT_GOODS_PRICE	0.774675
4023	AMT_CREDIT	AMT_ANNUITY	0.769630
2609	FLOORSMAX_AVG	FLOORSMIN_AVG	0.741484
2546	FLOORSMAX_MEDI	FLOORSMIN_MEDI	0.739444
2544	FLOORSMAX_MEDI	FLOORSMIN_AVG	0.739029
2611	FLOORSMAX_AVG	FLOORSMIN_MEDI	0.738896
2481	FLOORSMAX_MODE	FLOORSMIN_MEDI	0.728916
2479	FLOORSMAX_MODE	FLOORSMIN_AVG	0.728291
2480	FLOORSMAX_MODE	FLOORSMIN_MODE	0.725238
2545	FLOORSMAX_MEDI	FLOORSMIN_MODE	0.721918
2610	FLOORSMAX_AVG	FLOORSMIN_MODE	0.721288
2295	LIVINGAREA_AVG	BASEMENTAREA_AVG	0.692653
2294	LIVINGAREA_AVG	BASEMENTAREA_MEDI	0.692015
2424	LIVINGAREA_MEDI	BASEMENTAREA_MEDI	0.691390
2361	LIVINGAREA_MODE	BASEMENTAREA_MODE	0.690492
2425	LIVINGAREA_MEDI	BASEMENTAREA_AVG	0.689470
2359	LIVINGAREA_MODE	BASEMENTAREA_MEDI	0.680900
1904	APARTMENTS_MEDI	BASEMENTAREA_MEDI	0.680091
2627	FLOORSMAX_AVG	ELEVATORS_AVG	0.679146
1970	APARTMENTS_AVG	BASEMENTAREA_AVG	0.679102
1969	APARTMENTS_AVG	BASEMENTAREA_MEDI	0.678473
2036	APARTMENTS_MODE	BASEMENTAREA_MODE	0.678306
1905	APARTMENTS_MEDI	BASEMENTAREA_AVG	0.677979
2360	LIVINGAREA_MODE	BASEMENTAREA_AVG	0.677834
2562	FLOORSMAX_MEDI	ELEVATORS_AVG	0.676863
2626	FLOORSMAX_AVG	ELEVATORS_MEDI	0.675390
2561	FLOORSMAX_MEDI	ELEVATORS_MEDI	0.674838
2296	LIVINGAREA_AVG	BASEMENTAREA_MODE	0.674593
2426	LIVINGAREA_MEDI	BASEMENTAREA_MODE	0.674468
2880	TOTALAREA_MODE	BASEMENTAREA_AVG	0.674418
2879	TOTALAREA_MODE	BASEMENTAREA_MEDI	0.671195
2497	FLOORSMAX_MODE	ELEVATORS_AVG	0.670173
2034	APARTMENTS_MODE	BASEMENTAREA_MEDI	0.668578
2496	FLOORSMAX_MODE	ELEVATORS_MEDI	0.668348
2035	APARTMENTS_MODE	BASEMENTAREA_AVG	0.665976
1906	APARTMENTS_MEDI	BASEMENTAREA_MODE	0.664309
1971	APARTMENTS_AVG	BASEMENTAREA_MODE	0.661710
2498	FLOORSMAX_MODE	ELEVATORS_MODE	0.660255
2628	FLOORSMAX_AVG	ELEVATORS_MODE	0.655645
2231	ENTRANCES_MODE	BASEMENTAREA_MODE	0.654961
2563	FLOORSMAX_MEDI	ELEVATORS_MODE	0.654961
1371	BASEMENTAREA_MODE	LIVINGAPARTMENTS_MODE	0.654957
2166	ENTRANCES_AVG	BASEMENTAREA_MODE	0.654775
2164	ENTRANCES_AVG	BASEMENTAREA_MEDI	0.654314
2101	ENTRANCES_MEDI	BASEMENTAREA_MODE	0.653880
2099	ENTRANCES_MEDI	BASEMENTAREA_MEDI	0.652999
2165	ENTRANCES_AVG	BASEMENTAREA_AVG	0.652762
1241	BASEMENTAREA_MEDI	LIVINGAPARTMENTS_MODE	0.651968
1243	BASEMENTAREA_MEDI	LIVINGAPARTMENTS_MEDI	0.651337
2881	TOTALAREA_MODE	BASEMENTAREA_MODE	0.651152
1308	BASEMENTAREA_AVG	LIVINGAPARTMENTS_MEDI	0.649221
2100	ENTRANCES_MEDI	BASEMENTAREA_AVG	0.648736
1306	BASEMENTAREA_AVG	LIVINGAPARTMENTS_MODE	0.648552
1307	BASEMENTAREA_AVG	LIVINGAPARTMENTS_AVG	0.646971
1242	BASEMENTAREA_MEDI	LIVINGAPARTMENTS_AVG	0.646367
2900	TOTALAREA_MODE	FLOORSMAX_AVG	0.633906
2229	ENTRANCES_MODE	BASEMENTAREA_MEDI	0.632750
2899	TOTALAREA_MODE	FLOORSMAX_MEDI	0.631364
1373	BASEMENTAREA_MODE	LIVINGAPARTMENTS_MEDI	0.630983
2635	FLOORSMAX_AVG	LIVINGAREA_AVG	0.630503
2570	FLOORSMAX_MEDI	LIVINGAREA_AVG	0.628520
2230	ENTRANCES_MODE	BASEMENTAREA_AVG	0.628092
2637	FLOORSMAX_AVG	LIVINGAREA_MEDI	0.626982
2898	TOTALAREA_MODE	FLOORSMAX_MODE	0.626942
2572	FLOORSMAX_MEDI	LIVINGAREA_MEDI	0.626219
2505	FLOORSMAX_MODE	LIVINGAREA_AVG	0.626029
1372	BASEMENTAREA_MODE	LIVINGAPARTMENTS_AVG	0.625898
2373	LIVINGAREA_MODE	ENTRANCES_AVG	0.624529
2374	LIVINGAREA_MODE	ENTRANCES_MODE	0.624160
2507	FLOORSMAX_MODE	LIVINGAREA_MEDI	0.624093
2372	LIVINGAREA_MODE	ENTRANCES_MEDI	0.623937
2438	LIVINGAREA_MEDI	ENTRANCES_AVG	0.621125
2437	LIVINGAREA_MEDI	ENTRANCES_MEDI	0.620783
2308	LIVINGAREA_AVG	ENTRANCES_AVG	0.620648
2630	FLOORSMAX_AVG	APARTMENTS_AVG	0.618386
2307	LIVINGAREA_AVG	ENTRANCES_MEDI	0.616888
2565	FLOORSMAX_MEDI	APARTMENTS_AVG	0.616260
2241	ENTRANCES_MODE	APARTMENTS_MODE	0.616216
2629	FLOORSMAX_AVG	APARTMENTS_MEDI	0.615078
2564	FLOORSMAX_MEDI	APARTMENTS_MEDI	0.614179
2500	FLOORSMAX_MODE	APARTMENTS_AVG	0.614050
2111	ENTRANCES_MEDI	APARTMENTS_MODE	0.613458
2176	ENTRANCES_AVG	APARTMENTS_MODE	0.613216
2174	ENTRANCES_AVG	APARTMENTS_MEDI	0.612410
2109	ENTRANCES_MEDI	APARTMENTS_MEDI	0.612365
2499	FLOORSMAX_MODE	APARTMENTS_MEDI	0.612309
2175	ENTRANCES_AVG	APARTMENTS_AVG	0.612052
2110	ENTRANCES_MEDI	APARTMENTS_AVG	0.608536
2506	FLOORSMAX_MODE	LIVINGAREA_MODE	0.606002

	0.0	1.0	variable	sum_null_values	porcentaje_sum_null_values
0	0.914444	0.085556	COMMONAREA_MEDI	172239	0.700136
1	0.914444	0.085556	COMMONAREA_AVG	172239	0.700136
2	0.914444	0.085556	COMMONAREA_MODE	172239	0.700136
3	0.914329	0.085671	NONLIVINGAPARTMENTS_MODE	171119	0.695583
4	0.914329	0.085671	NONLIVINGAPARTMENTS_AVG	171119	0.695583
5	0.914329	0.085671	NONLIVINGAPARTMENTS_MEDI	171119	0.695583
6	0.913941	0.086059	LIVINGAPARTMENTS_MODE	168455	0.684754
7	0.913941	0.086059	LIVINGAPARTMENTS_AVG	168455	0.684754
8	0.913941	0.086059	LIVINGAPARTMENTS_MEDI	168455	0.684754
9	0.913856	0.086144	FLOORSMIN_AVG	167232	0.679783
10	0.913856	0.086144	FLOORSMIN_MODE	167232	0.679783
11	0.913856	0.086144	FLOORSMIN_MEDI	167232	0.679783
12	0.913336	0.086664	YEARS_BUILD_MEDI	163886	0.666182
13	0.913336	0.086664	YEARS_BUILD_MODE	163886	0.666182
14	0.913336	0.086664	YEARS_BUILD_AVG	163886	0.666182
15	0.914648	0.085352	OWN_CAR_AGE	162492	0.660515
16	0.912131	0.087869	LANDAREA_MEDI	146388	0.595054
17	0.912131	0.087869	LANDAREA_MODE	146388	0.595054
18	0.912131	0.087869	LANDAREA_AVG	146388	0.595054
19	0.911248	0.088752	BASEMENTAREA_MEDI	144312	0.586615
20	0.911248	0.088752	BASEMENTAREA_AVG	144312	0.586615
21	0.911248	0.088752	BASEMENTAREA_MODE	144312	0.586615
22	0.914680	0.085320	EXT_SOURCE_1	138560	0.563234
23	0.909920	0.090080	NONLIVINGAREA_MODE	136057	0.553059
24	0.909920	0.090080	NONLIVINGAREA_AVG	136057	0.553059
25	0.909920	0.090080	NONLIVINGAREA_MEDI	136057	0.553059
26	0.909254	0.090746	ELEVATORS_MEDI	131355	0.533946
27	0.909254	0.090746	ELEVATORS_AVG	131355	0.533946
28	0.909254	0.090746	ELEVATORS_MODE	131355	0.533946
29	0.908839	0.091161	APARTMENTS_MEDI	125098	0.508512
30	0.908839	0.091161	APARTMENTS_AVG	125098	0.508512
31	0.908839	0.091161	APARTMENTS_MODE	125098	0.508512
32	0.908527	0.091473	ENTRANCES_MEDI	124059	0.504288
33	0.908527	0.091473	ENTRANCES_AVG	124059	0.504288
34	0.908527	0.091473	ENTRANCES_MODE	124059	0.504288
35	0.908865	0.091135	LIVINGAREA_AVG	123739	0.502988
36	0.908865	0.091135	LIVINGAREA_MODE	123739	0.502988
37	0.908865	0.091135	LIVINGAREA_MEDI	123739	0.502988
38	0.908445	0.091555	FLOORSMAX_MODE	122647	0.498549
39	0.908445	0.091555	FLOORSMAX_MEDI	122647	0.498549
40	0.908445	0.091555	FLOORSMAX_AVG	122647	0.498549
41	0.908302	0.091698	YEARS_BEGINEXPLUATATION_MODE	120254	0.488822
42	0.908302	0.091698	YEARS_BEGINEXPLUATATION_MEDI	120254	0.488822
43	0.908302	0.091698	YEARS_BEGINEXPLUATATION_AVG	120254	0.488822
44	0.908066	0.091934	TOTALAREA_MODE	118987	0.483671
45	0.906547	0.093453	EXT_SOURCE_3	48784	0.198302
46	0.896163	0.103837	AMT_REQ_CREDIT_BUREAU_HOUR	33206	0.134979
47	0.896163	0.103837	AMT_REQ_CREDIT_BUREAU_DAY	33206	0.134979
48	0.896163	0.103837	AMT_REQ_CREDIT_BUREAU_WEEK	33206	0.134979
49	0.896163	0.103837	AMT_REQ_CREDIT_BUREAU_MON	33206	0.134979
50	0.896163	0.103837	AMT_REQ_CREDIT_BUREAU_QRT	33206	0.134979
51	0.896163	0.103837	AMT_REQ_CREDIT_BUREAU_YEAR	33206	0.134979
52	0.964461	0.035539	OBS_30_CNT_SOCIAL_CIRCLE	816	0.003317
53	0.964461	0.035539	DEF_30_CNT_SOCIAL_CIRCLE	816	0.003317
54	0.964461	0.035539	OBS_60_CNT_SOCIAL_CIRCLE	816	0.003317
55	0.964461	0.035539	DEF_60_CNT_SOCIAL_CIRCLE	816	0.003317
56	0.925512	0.074488	EXT_SOURCE_2	537	0.002183
57	0.917808	0.082192	AMT_GOODS_PRICE	219	0.000890
58	1.000000	0.000000	AMT_ANNUITY	8	0.000033
59	1.000000	0.000000	CNT_FAM_MEMBERS	1	0.000004
60	1.000000	0.000000	DAYS_LAST_PHONE_CHANGE	1	0.000004

	nulos_filas	porcentaje_filas
69707	61	0.5
133770	61	0.5
150206	61	0.5
197736	61	0.5
269492	61	0.5